{"version":1,"generatedAt":"2026-11-04T08:02:41.360Z","since":"2026-10-27","by":"added","count":28,"total":28,"papers":[{"id":"ntrs:20260007180","title":"Multivariate Subspace Analysis for Sensor Fusion in Subsurface Planetary Drilling Fault and Anomaly Detection","authors":["Sarah Boelter","Rene Mai","Greta Brown","Thomas Stucky","Brian Glass","Marybeth Wilhelm","Maria Gini"],"abstract":"In extraterrestrial planetary environments, environmental, computing, energy, and operational constraints require robotic agents to complete tasks without human supervision. Specialized extraterrestrial robotic drilling agents like TRIDENT currently lack autonomous capabilities, primarily because research into subsurface environment representation for autonomous drilling remains in its early stages. We build upon previous field-validated work using subspace analysis methods to estimate drilling faults from drill avionics telemetry, and extend it to a multivariate subsurface anomaly detection method that leverages sensor fusion of avionics telemetry from extraterrestrial drilling robots. This method was evaluated at Mars planetary analog sites in Antarctica.","published":"2026-11-03","addedAt":"2026-11-03","year":2026,"venue":{"id":"isparo","label":"iSpaRo 2026","year":2026,"isWorkshop":false,"raw":"ISpaRo Conference"},"intersection":"both","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"},{"id":"isru-construction","label":"ISRU & CONSTRUCTION"}],"techniques":[{"id":"manipulation","code":"T1","label":"Manipulation & Grasping","source":"text","via":["title:drill","abstract:drill"]},{"id":"perception","code":"T5","label":"Perception & State Estimation","source":"text","via":["title:sensor fusion"]},{"id":"autonomy-ops","code":"T10","label":"Autonomy, Task Planning & Operations","source":"text","via":["title:anomaly detection"]}],"missions":[],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Lunar and Planetary Science and Exploration"],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20260007180/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20260007180/downloads/iSpaRo.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"ntrs:20250011718","title":"EDL Concept of Operations for the Dragonfly Rotorcraft Mission to Titan","authors":["Michael J Wright","Robin Vaughan","Michael Robbins","Karl T Edquist"],"abstract":"Dragonfly, NASA’s fourth New Frontiers mission, will deliver a rotorcraft to Titan for mobility and repeated in-situ science. This paper presents the concept of operations for the entry, descent, lander release, and first-flight portions of the mission, coupling an open-loop, spin-stabilized entry vehicle with a closed-loop aerial mobility system. After cruise-stage separation, the 4.5 m aeroshell executes entry at ~7.4 km/s, followed by deployment of a drogue parachute at Mach 1.5. The vehicle then descends over the next 100 minutes until the main parachute is barometrically deployed near 5 km pressure altitude. The heatshield is released two minutes after main deploy, exposing the rotorcraft to the titan environment. Key events during “preparation for powered flight” include thermal-loop reconfiguration, a gravity-driven pose to expose the rotors, rotor-based yaw despin, and lander release at ~900 m altitude. Post-release, the lander transitions from free fall into controlled flight, descends to a search altitude, performs lidar/NavCam hazard mapping and breadcrumb navigation, and executes terminal descent and touchdown. Direct-to-Earth tones provide critical-event coverage with planned inhibits for corona and shock events. Successful delivery to the surface enables Dragonfly’s three-year science mission to explore the region near Selk impact crater.","published":"2026-01-12","addedAt":"2026-11-03","year":2026,"venue":{"id":"aiaa-scitech","label":"AIAA SciTech 2026","year":2026,"isWorkshop":false,"raw":"AIAA SciTech Forum"},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"surface-mobility","code":"T2","label":"Surface Mobility & Terramechanics","source":"official","via":["ntrs-keyword:Rotorcraft","title:rotorcraft","abstract:rotorcraft"]}],"missions":[{"id":"dragonfly","name":"Dragonfly","fields":["title","abstract","keywords"],"page":"https://space-robots.org/papers/missions/dragonfly/"}],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Aerodynamics","Aircraft Design, Testing and Performance","Lunar and Planetary Science and Exploration"],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20250011718/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20250011718/downloads/2026_SciTech_Dragonfly_EDL_Wright%20Paper_v2.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"ntrs:20250010356","title":"In-flight System Identification of the Ingenuity Mars Helicopter","authors":["Tove S Aagren","Allen W Ruan","Carlos Malpica","Shannah Withrow-Maser","Larry Meyn"],"abstract":"The 68th and 69th flights of NASA’s Ingenuity Mars Helicopter marked the first dedicated system identification flights of a powered-lift aircraft on another planet. Frequency-domain techniques, similar to those utilized in the Earth-based system identification campaign for Ingenuity, were employed for the first time under free-flight conditions on Mars. Chirp signals were injected into the swashplate cyclic controls for both legs of the two out-and-back flights. Frequency responses were computed from the flight data, using both the Direct Method (DM) and the Joint-Input-Output (JIO) approach, for the identification of stability and control derivatives in forward flight conditions. The resulting identified state-space models were compared against existing flight dynamics simulation models, showing excellent correlation in the higher frequency range. External disturbances were seen to introduce a bias in the identified lower frequency responses, which was partially mitigated using the JIO method. These findings will inform future modeling and flight testing efforts of Mars rotorcraft.","published":"2025-11-28","addedAt":"2026-11-03","year":2025,"venue":{"id":"jgcd","label":"JGCD 2025","year":2025,"isWorkshop":false,"raw":"AIAA Journal Of Guidance, Control and Dynamics"},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"surface-mobility","code":"T2","label":"Surface Mobility & Terramechanics","source":"official","via":["ntrs-keyword:Helicopter","title:helicopter","abstract:helicopter","abstract:rotorcraft"]}],"missions":[{"id":"ingenuity","name":"Ingenuity Mars Helicopter","fields":["title","abstract","keywords"],"page":"https://space-robots.org/papers/missions/ingenuity/"}],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Aeronautics (General)"],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20250010356/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20250010356/downloads/In_flight_Aagren.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"ntrs:20240008174","title":"Comparing Planetary Helicopter Brownout Observations with Numerical Predictions for Ingenuity","authors":["Denis-Gabriel Caprace","Ethan Baker","Patricia Ventura Diaz","Seokkwan Yoon"],"abstract":"Ingenuity is considered the Martian equivalent of the Wright Flyer. Its sustained flights on Mars proved that non-terrestrial powered flight is possible, even on planets with thin atmospheres. However, Ingenuity was small and carried no payload. Future planetary rotorcraft will be bigger and heavier to accomplish new scientific and exploratory goals. On dusty planets, the associated increase in rotor thrust poses a potential challenge well known to helicopter pilots who have flown in desert environments: dust clouds forming during take-off and landing due to rotor wash, referred to as brownout. This work evaluates the effectiveness of existing numerical simulation models in predicting the severity of this phenomenon for planetary rotorcraft. We compare the output of a CFD-based dust mobilization model with data recently gathered using image processing techniques of Ingenuity’s flights. The simulation results suggest that the sensitivity of the wall friction velocity to the rotorcraft height may be lower than previously assessed. We further evaluate the sensitivity of the total mass of mobilized dust to other model parameters such as the sandblasting efficiency and the saltation threshold velocity. Finally, we discuss the limitations of this comparison due to uncertainty in both the observation and the numerical model.","published":"2024-07-29","addedAt":"2026-11-03","year":2024,"venue":{"id":"other","label":"AIAA Aviation Forum","year":2024,"isWorkshop":false,"raw":"AIAA Aviation Forum"},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"surface-mobility","code":"T2","label":"Surface Mobility & Terramechanics","source":"text","via":["title:helicopter","abstract:rotorcraft","abstract:helicopter"]}],"missions":[{"id":"ingenuity","name":"Ingenuity Mars Helicopter","fields":["title","abstract"],"page":"https://space-robots.org/papers/missions/ingenuity/"}],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Aeronautics (General)"],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20240008174/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20240008174/downloads/Paper_Aviation2024_Caprace.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"doi:10.1109/space63117.2024.10668290","title":"Design of a Series-Elastic Actuator for a Humanoid Robot for Space Applications","authors":["Shubhankar Riswadkar","S Barat","Harish J. Palanthandalam‐Madapusi"],"abstract":"Robots in space operate in highly unstructured environments and terrains due to which compliant actuators are preferred over rigid geared actuators. Series Elastic Actuators (SEAs) are a common choice for compliant actuation as they protect both the robot and the environment from unexpected collisions. The low output impedance due to the integration of a passive compliance element makes it capable of effective stable interactions with the environment. This paper describes the development of the Series Elastic Actuator (SEA) for space application through key considerations such as transparency, torque tracking bandwidth, and impedance rendering.","published":"2024-07-22","addedAt":"2026-11-03","year":2024,"venue":{"id":"ieee-space","label":"IEEE SPACE 2024","year":2024,"isWorkshop":false,"raw":"IEEE Space, Aerospace and Defence Conference (SPACE)"},"intersection":"robotics","domains":[],"techniques":[{"id":"design-hardware","code":"T14","label":"Design, Mechanisms & Hardware","source":"official","via":["openalex-topic:Soft Robotics and Applications","openalex-keyword:series elastic actuator","title:actuator","abstract:actuator"]},{"id":"planning-navigation","code":"T6","label":"Motion Planning & Navigation","source":"official","via":["openalex-keyword:collision avoidance"]},{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["openalex-keyword:impedance control"]}],"missions":[],"indexedAs":{"arxiv":[],"openalexTopics":[{"id":"T10868","name":"Soft Robotics and Applications","score":0.682},{"id":"T10571","name":"Robotic Mechanisms and Dynamics","score":0.101},{"id":"T13644","name":"Control Systems in Engineering","score":0.05}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/doi%3A10.1109/space63117.2024.10668290","arxiv":null,"pdf":null,"html":null,"doi":"https://doi.org/10.1109/space63117.2024.10668290","code":null,"project":null}},{"id":"ntrs:20220006002","title":"Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery","authors":["Ryan T. Scott","Lovorka Degoricija","Lauren M. Sanders","Danielle K. Lopez","Samrawit G. Gebre","Jonathan Galazka","Ahleah Rohr Daniel","Sylvain V. Costes"],"abstract":"The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA \"Open Science Data Repositories (OSDR)\" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to \"big data\" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.","published":"2022-07-25","addedAt":"2026-11-03","year":2022,"venue":{"id":"other","label":"International Space Station Research and Development Conference (ISSRDC)","year":2022,"isWorkshop":false,"raw":"International Space Station Research and Development Conference (ISSRDC)"},"intersection":"ai","domains":[],"techniques":[],"missions":[],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Space Sciences (General)"],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20220006002/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20220008713/downloads/RADRES_2022.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"ntrs:20210020457","title":"Towards Autonomous Lunar Resource Excavation via Reinforcement Learning","authors":["Joseph M Cloud","Rolando Nieves","Adam Duke","Thomas Muller","Nashir A Janmohamed","Brad C Buckles","Michael A Dupuis"],"abstract":"To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.","published":"2021-11-15","addedAt":"2026-11-03","year":2021,"venue":{"id":"aiaa-space","label":"AIAA SPACE / ASCEND 2021","year":2021,"isWorkshop":false,"raw":"ASCEND Conference"},"intersection":"both","domains":[{"id":"isru-construction","label":"ISRU & CONSTRUCTION"}],"techniques":[{"id":"learning","code":"T8","label":"Learning-based Methods","source":"official","via":["ntrs-keyword:reinforcement learning","ntrs-keyword:machine learning","title:reinforcement learning","abstract:reinforcement learning"]}],"missions":[],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Cybernetics, Artificial Intelligence and Robotics"],"ieeeTerms":[]},"cardTag":"RESEARCH // ISRU & CONSTRUCTION","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20210020457/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20210020457/downloads/ice_rassor_learning_excavation.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"doi:10.1109/aero.2019.8741689","title":"Int-Ball: Crew-Supportive Autonomous Mobile Camera Robot on ISS/JEM","authors":["Shinji Mitani","Masayuki Goto","Ryo Konomura","Yasushi Shoji","Keiji Hagiwara","Shuhei Shigeto","Nobutaka Tanishima"],"abstract":"This paper describes the development of The Japanese Experiment Module (called JEM) Internal Ball Camera (called Int-Ball) that autonomously moves inside the JEM pressurization section and shoots image and video of the object. The Int-Ball was launched as a payload of Dragon spacecraft by SpaceX Falcon 9 on June 4, 2017. First flight control demonstration was successfully conducted in JEM pressurization section. This paper also describes the initial checkout result.","published":"2019-03-01","addedAt":"2026-11-03","year":2019,"venue":{"id":"ieee-aero","label":"IEEE Aerospace 2019","year":2019,"isWorkshop":false,"raw":"IEEE Aerospace Conference (AERO)"},"intersection":"robotics","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[{"id":"free-flyers","code":"T3","label":"Microgravity Mobility & Free-Flyers","source":"text","via":["title:int-ball","abstract:int-ball"]},{"id":"hri-teleoperation","code":"T11","label":"HRI & Teleoperation","source":"text","via":["title:crew"]}],"missions":[{"id":"int-ball","name":"Int-Ball","fields":["title","abstract"],"page":"https://space-robots.org/papers/missions/int-ball/"}],"indexedAs":{"arxiv":[],"openalexTopics":[{"id":"T11701","name":"Space Satellite Systems and Control","score":0.398},{"id":"T12449","name":"Spacecraft Design and Technology","score":0.382},{"id":"T14214","name":"Space Exploration and Technology","score":0.109}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":48,"links":{"page":"https://space-robots.org/papers/paper/doi%3A10.1109/aero.2019.8741689","arxiv":null,"pdf":null,"html":null,"doi":"https://doi.org/10.1109/aero.2019.8741689","code":null,"project":null}},{"id":"doi:10.24247/ijmperdfeb201921","title":"An Inverse Kinematics Analysis of Space Station Remote Manipulator System (SSRMS) Using Genetic Algorithms","authors":["Satyendra Jaladi","TJPRC"],"abstract":"The importance of RobotArms (Serial Manipulators) is increasing rapidly in the last few years in space, industrial and medical applications.An important part of Robot Arms is to achieve desired pose (position and orientation), in order to accomplish this very good knowledge of inverse kinematics is needed.Getting a solution to Inverse kinematics of the Robot Arms has been considered as a mature problem which is thoroughly researched and is also on the focus of various research and developments in Robotics field.SSRMS (a redundant manipulator) has total 7 revolute joints.They can be structured as shoulder joints, elbow joints and wrist joints (end effector).This paper presents biologically-inspired Genetic Algorithms (optimization) approach for solving Inverse kinematics problem of SSRMS.In this paperGA solver from MATLAB's (optimization toolbox) is used to find the joint angles of SSRMS robot, solver parameters such as population size, elite count, crossover fraction, etc. are selected to achieve maximum performance resulting in optimum joint variables.","published":"2019-01-01","addedAt":"2026-11-03","year":2019,"venue":{"id":"other","label":"International journal of mechanical and production engineering research and development","year":2019,"isWorkshop":false,"raw":"International journal of mechanical and production engineering research and development"},"intersection":"robotics","domains":[],"techniques":[{"id":"manipulation","code":"T1","label":"Manipulation & Grasping","source":"official","via":["openalex-keyword:Space Station Remote Manipulator System","openalex-keyword:redundant manipulators","openalex-keyword:serial manipulators","title:manipulat"]},{"id":"design-hardware","code":"T14","label":"Design, Mechanisms & Hardware","source":"official","via":["openalex-topic:Soft Robotics and Applications"]}],"missions":[{"id":"canadarm","name":"Canadarm2, Dextre & Canadarm3","fields":["title","abstract","keywords"],"page":"https://space-robots.org/papers/missions/canadarm/"}],"indexedAs":{"arxiv":[],"openalexTopics":[{"id":"T10571","name":"Robotic Mechanisms and Dynamics","score":1},{"id":"T10868","name":"Soft Robotics and Applications","score":0},{"id":"T11394","name":"Dynamics and Control of Mechanical Systems","score":0}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":3,"links":{"page":"https://space-robots.org/papers/paper/doi%3A10.24247/ijmperdfeb201921/","arxiv":null,"pdf":"https://doi.org/10.24247/ijmperdfeb201921","html":null,"doi":"https://doi.org/10.24247/ijmperdfeb201921","code":null,"project":null}},{"id":"ntrs:20180001747","title":"Robonaut 2 and Watson: Cognitive Dexterity for Future Exploration","authors":["Julia M. Badger","Philip Strawser","Logan Farrell","S. Michael Goza","Charles A. Claunch","Raphael Chancey","Russell Potapinski"],"abstract":"Future exploration missions will dictate a level of autonomy never before experienced in human spaceflight. Mission plans involving the uncrewed phases of complex human spacecraft in deep space will require a coordinated autonomous capability to be able to maintain the spacecraft when ground control is not available. One promising direction involves embedding intelligence into the system design both through the employment of state-of-the-art system engineering principles as well as through the creation of a cognitive network between a smart spacecraft or habitat and embodiments of cognitive agents. The work described here details efforts to integrate IBM's Watson and other cognitive computing services into NASA Johnson Space Center (JSC)'s Robonaut 2 (R2) anthropomorphic robot. This paper also discusses future directions this work will take. A cognitive spacecraft management system that is able to seamlessly collect data from subsystems, determine corrective actions, and provide commands to enable those actions is the end goal. These commands could be to embedded spacecraft systems or to a set of robotic assets that are tied into the cognitive system. An exciting collaboration with Woodside provides a promising Earth-bound testing analog, as controlling and maintaining not normally manned off-shore platforms have similar constraints to the space missions described.","published":"2018-03-03","addedAt":"2026-11-03","year":2018,"venue":{"id":"ieee-aero","label":"IEEE Aerospace 2018","year":2018,"isWorkshop":false,"raw":"IEEE Aerospace Conference"},"intersection":"robotics","domains":[],"techniques":[],"missions":[{"id":"robonaut","name":"Robonaut 2","fields":["title","abstract"],"page":"https://space-robots.org/papers/missions/robonaut/"}],"indexedAs":{"arxiv":[],"openalexTopics":[],"ntrsSubjects":["Cybernetics, Artificial Intelligence and Robotics"],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/ntrs%3A20180001747/","arxiv":null,"pdf":"https://ntrs.nasa.gov/api/citations/20180001747/downloads/20180001747.pdf","html":null,"doi":null,"code":null,"project":null}},{"id":"2611.02809","title":"Offline Approximate Value Iteration: A Reinforcement Learning Approach for Satellite Constellation Deployment","authors":["Tristan Leuridan","Yiping Fang","Adam Abdin"],"abstract":"This paper introduces a reinforcement learning-based approximate dynamic programming approach to optimize the deployment of Walker-type satellite constellations under uncertainties, including launch failures and satellite malfunctions. We formulate the problem as a Markov Decision Process that incorporates low-injection orbit launches, continuous low-thrust orbit raising, and variable satellite loads. We adopt the offline approximate value iteration solution method, using linear function approximation with basis functions and action pruning strategies to substantially reduce the computational and memory requirements for finding optimal decision policies. Preliminary results demonstrate convergence to policies with low expected costs and deployment times, both of which are lower than those of a rolling-horizon baseline, showcasing a novel application of approximate dynamic programming in space logistics.","published":"2026-11-02","addedAt":"2026-11-02","year":2026,"venue":{"id":"other","label":"3rd Conference on AI in and for Space (SPAICE 2026), European Space Agency (ESA), Oct 2026, Noordwijk, Netherlands","year":2026,"isWorkshop":false,"raw":"3rd Conference on AI in and for Space (SPAICE 2026), European Space Agency (ESA), Oct 2026, Noordwijk, Netherlands"},"intersection":"ai","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["arxiv:math.OC"]},{"id":"learning","code":"T8","label":"Learning-based Methods","source":"text","via":["title:reinforcement learning"]}],"missions":[],"indexedAs":{"arxiv":["cs.AI","math.OC"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.02809","arxiv":"https://arxiv.org/abs/2611.02809","pdf":"https://arxiv.org/pdf/2611.02809","html":"https://arxiv.org/html/2611.02809","doi":null,"code":null,"project":null}},{"id":"2611.02872","title":"Design and Experimental Validation of the Separation and Deployment System for the CHERI Lunar Micro-Rovers","authors":["Ahmet Mustafa Kangal","Oğuzhan Şahan","Teoman Efe Aycan","Görkem Egemen Güloğlu","Halil Ersin Söken","Ali Emre Turgut","Uluç Saranlı","Mustafa Mert Ankaralı"],"abstract":"Reliable separation mechanisms are critical for planetary micro-rovers, which must withstand launch vibrations and then eject the vehicle clear of the lander. This paper presents the design, optimization, and testing of the separation system of the CHERI (Challenging Environment Exploration Rovers for Intelligence) lunar micro-rovers. Sized for a 5 kg vehicle, the assembly comprises an aluminum ground plate, a central non-explosive hold-down and release mechanism, and an asymmetric four-spring ejection array. Initial separation trials revealed a velocity deficit caused by energy absorption in the flexible wheel-legs and chassis during the ejection stroke. This loss was represented as equivalent chassis damping in a multi-body dynamic model, and the springs were re-optimized accordingly. Early hardware iterations failed vibration testing, first through spring jamming in the guide housings and then through a 34 Hz fundamental mode of the flat ground plate. A density-based topology optimization raised the fundamental frequency to 84 Hz, above the 70 Hz requirement of the lander, within a 1.6 kg mass budget. The final configuration passed acceptance-level sinusoidal vibration, random vibration, and shock testing without structural degradation. Separation tests with a weight-matched one-sixth mass model, tracked by optical motion capture, confirmed clearance of the lander and reproduced the simulated free flight to within the run-to-run variation of the measured ejection velocity. These tests further showed that landing outcome is dominated by touchdown and settling dynamics, indicating that the residual pitch rate is the main controllable design parameter and confirming self-righting recovery as an integral element of the deployment architecture.","published":"2026-11-02","addedAt":"2026-11-02","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.02872","arxiv":"https://arxiv.org/abs/2611.02872","pdf":"https://arxiv.org/pdf/2611.02872","html":"https://arxiv.org/html/2611.02872","doi":null,"code":null,"project":null}},{"id":"2611.02922","title":"An intelligent satellite must do nothing well","authors":["Fabrizio Pilone","Giordana Bucchioni"],"abstract":"Autonomous inspection of a resident space object is usually posed as a contest between classical next-best-view planning and reinforcement learning. We argue that the choice of algorithm is the least informative of the design axes. An inspector spacecraft has no rest state: when it stops thrusting, orbital dynamics carry it along a natural trajectory that can be used to observe the target at no fuel cost. On a controlled ladder in which one design axis is varied at a time, fuel efficiency turns out to be set by three things: whether the agent is allowed to drift, whether its state records where drifting has taken it, and whether its planning horizon is long enough for the benefit to appear. Removing any one of them doubles the fuel bill or more. The algorithm still matters, but through what it can represent: a model-based agent whose learned world model ignores fuel spends several times more of it. Finally, no learned policy is safe by construction, and a control barrier function filter beneath the decision layer removed every keep-out-zone violation in two thousand evaluation episodes.","published":"2026-11-02","addedAt":"2026-11-02","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"ai","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.02922","arxiv":"https://arxiv.org/abs/2611.02922","pdf":"https://arxiv.org/pdf/2611.02922","html":"https://arxiv.org/html/2611.02922","doi":null,"code":null,"project":null}},{"id":"2611.03430","title":"Evaluating Depth-Augmented Visual Features for Cross-Dataset Lunar Traversability Estimation","authors":["Jan Baturo","Bartosz Ptak"],"abstract":"Autonomous mobility is essential for sustained lunar surface operations, as communication delays and line-of-sight occlusions limit continuous teleoperation. Although recent traversability estimation methods increasingly rely on deep learning, the domain gap between terrestrial analogue environments and real lunar terrain can limit their generalisation, and real lunar traversability labels are scarce, making fully supervised training difficult. To address these challenges, we build a weakly supervised framework trained on sparse pairwise annotations and study whether augmenting visual features with depth-derived geometric information: depth, surface normals, and combined geometric descriptors. We evaluate the models in both in-domain and cross-domain settings using two terrestrial analogue datasets and the real lunar Chang'E PCAM dataset. Contrary to expectations, we find that naive geometric augmentation yields no consistent improvement over a strong visual-only baseline in either setting, with effects small relative to seed-to-seed variance. Instead, the choice of training source emerges as the dominant factor in transfer quality: models trained on domain-relevant sources transfer substantially better to real lunar imagery than the geometric fusion strategy improves any single model. Our evaluation runs onboard a resource-constrained edge device and provides a controlled baseline for designing more expressive multimodal fusion architectures in future lunar perception systems.","published":"2026-11-02","addedAt":"2026-11-02","year":2026,"venue":{"id":"isparo","label":"iSpaRo 2026","year":2026,"isWorkshop":false,"raw":null},"intersection":"both","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"surface-mobility","code":"T2","label":"Surface Mobility & Terramechanics","source":"text","via":["title:traversab","abstract:traversab"]},{"id":"simulation-testbeds","code":"T13","label":"Simulation, Digital Twins & Benchmarks","source":"text","via":["title:dataset","abstract:dataset"]},{"id":"perception","code":"T5","label":"Perception & State Estimation","source":"official","via":["arxiv:cs.CV"]}],"missions":[{"id":"change-yutu","name":"Chang'e & Yutu","fields":["abstract"],"page":"https://space-robots.org/papers/missions/change-yutu/"}],"indexedAs":{"arxiv":["cs.CV","cs.RO"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.03430","arxiv":"https://arxiv.org/abs/2611.03430","pdf":"https://arxiv.org/pdf/2611.03430","html":"https://arxiv.org/html/2611.03430","doi":null,"code":null,"project":null}},{"id":"2611.00870","title":"A Reproducible Reconstruction and Evaluation Framework for Photorealistic Lunar Analog Scene Reconstruction Toward Digital Twins","authors":["Antar Mazumder","Ryan Hartzell","Luke Boyd","Alexander Robinson","Neha Sharma","Chance Carnival","Frances Zhu"],"abstract":"In this paper, we present a reconstruction pipeline that empirically quantifies the photorealistic scene reconstruction accuracy from both human-controlled and rover mast camera monocular feeds against physical ground truth as a function of Lunar traversal effects (e.g., motion blur, haze due to lofted regolith, trajectory bias, and sparse views). First, we benchmark six variants of 3D Gaussian Splatting (3DGS) across three Lunar analog scenes on held-out test sets using standard photometric metrics. We then establish how under traversal effects, this test-set-specific Novel View Synthesis (NVS) performance does not proportionally scale to the same photometric metrics in the reconstructed DTs. Our results quantify that splat training metrics alone are insufficient predictors of Cross-Reconstruction Photorealistic NVS Fidelity (CRPNVS), and that factors such as rover trajectory bias and geometric scene coverage play a critical role in determining the photorealistic quality of the reconstructed scenes targeting Lunar analog Digital Twins. Additionally, we pilot study Coverage Score ($S_c$), a geometry-based diagnostic derived from COLMAP poses and point cloud structure that provides a meaningful heuristic about the highest achievable CRPNVS structural fidelity, independently of splat training performance in the absence of a human-controlled reference. These findings establish a standardized, human control-referenced evaluation pipeline for photorealistic scene reconstruction for Lunar-analog Digital Twin development and future planetary exploration. Code and project page: https://rose-lab-admin.github.io/rose-lunar-dt-rec","published":"2026-10-31","addedAt":"2026-10-31","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"simulation-testbeds","code":"T13","label":"Simulation, Digital Twins & Benchmarks","source":"text","via":["title:digital twin","abstract:benchmark","abstract:digital twin"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // TESTBEDS & SIMULATION","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.00870","arxiv":"https://arxiv.org/abs/2611.00870","pdf":"https://arxiv.org/pdf/2611.00870","html":"https://arxiv.org/html/2611.00870","doi":null,"code":null,"project":null}},{"id":"2611.00909","title":"Clockwork: Scheduling Compound AI Systems in Space Data Centers","authors":["Milos Gravara","Andrija Stanisic","Stefan Nastic"],"abstract":"Space data centers built on LEO constellations create an opportunity to serve Compound AI workflows close to where observations are captured. Serving such a workflow for an area of interest (AoI) requires deciding which orbital planes to provision and where to place each workflow component, while orbital motion continuously changes which satellites can serve the AoI. Provisioning must therefore be planned as a sequence of decisions over time, and these decisions are coupled because changing component placements or activating new planes incurs costs. To address this, we present Clockwork, a framework for scheduling Compound AI workflows on space data centers under end-to-end latency SLOs. Clockwork has two components: i) Castor jointly selects active planes and component placements for each constellation snapshot, while ii) Pollux connects Castor's candidate provisioning states across predicted orbital evolution to construct a low-cost schedule over time. On our experimental two-shell constellation, Castor serves 81% of instances at a 300 ms SLO, while Pollux reduces mean total scheduling cost by 56% on average and by up to 63% compared to heuristic baselines.","published":"2026-10-31","addedAt":"2026-10-31","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"ai","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[{"id":"autonomy-ops","code":"T10","label":"Autonomy, Task Planning & Operations","source":"text","via":["arxiv:cs.AI","title:scheduling","abstract:scheduling"]}],"missions":[],"indexedAs":{"arxiv":["cs.DC","cs.AI","cs.NI"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.00909","arxiv":"https://arxiv.org/abs/2611.00909","pdf":"https://arxiv.org/pdf/2611.00909","html":"https://arxiv.org/html/2611.00909","doi":null,"code":null,"project":null}},{"id":"2611.01001","title":"GURU: A Hierarchical Neuro-Symbolic Multi-Agent System for Verification-Aware Automation of Mission-Critical Digital Engineering and Operations","authors":["Allan Grosvenor","Anton Zemlyansky","Abdul Wahab","Kyrylo Bohachov","Aras Dogan","Ryland Adams","Dwyer Deighan"],"abstract":"An AI can find a safe action faster than any human, but it must never decide what counts as safe. We present GURU, a hierarchical neuro-symbolic multi-agent system for mission-critical digital engineering and operations, built on one partition of authority: learned models propose, rank, and accelerate; a symbolic rules engine checks every consequential action against hard limits before it executes; and validated physics confirms and decides. GURU runs a workflow as a closed control loop, with Navigation, Action, and Planning agents sharing one Common Internal Language and one training factory. Nuclear, aviation, and national-security guidance now requires this pattern; a harnessed language model keeps the model at the center, GURU puts a validated reference there. In computational fluid dynamics, the workflow raised the converged-simulation rate on unseen aircraft geometries from under 1 percent to 74 percent over 100 single-attempt cases, ran 54 live cases to Mach 6 on three facilities, and an ablation put the symbolic layer at 13 points of boundary-layer capture. Knowledge-graph retrieval and role adapters raised ParaView control success from 55 to 75 percent. In space domain awareness, where a language model cannot be trusted to decide, a frozen self-supervised world model under validated propagation scored highest of five methods at conjunction risk, including fully fine-tuned language models, with several-times-lower seed variance: the trustworthy design gives up nothing in accuracy, and the recipe transferred to reactor anomaly detection. A radar-to-avoidance debris pipeline was demonstrated in stages on a 2026 reentry; the hierarchy coordinates simulated teams of 200 aircraft. Training scaled near-linearly past 1,000 Aurora nodes and ran at 8,000. The approach extends to a control-grade microreactor digital twin with validated physics, not a learned model, as the safety basis.","published":"2026-10-31","addedAt":"2026-10-31","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"ai","domains":[],"techniques":[{"id":"multi-robot","code":"T12","label":"Multi-Robot & Swarms","source":"official","via":["arxiv:cs.MA","title:multi-agent"]},{"id":"autonomy-ops","code":"T10","label":"Autonomy, Task Planning & Operations","source":"text","via":["arxiv:cs.AI","title:verification"]},{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["arxiv:eess.SY"]}],"missions":[],"indexedAs":{"arxiv":["cs.AI","cs.CE","cs.MA","cs.SY","eess.SY"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.01001","arxiv":"https://arxiv.org/abs/2611.01001","pdf":"https://arxiv.org/pdf/2611.01001","html":"https://arxiv.org/html/2611.01001","doi":null,"code":null,"project":null}},{"id":"2611.01233","title":"STLT: A Novel System for Energy-Aware Distributed Inference in the Edge-Cloud-Space Continuum","authors":["Andrija Stanisic","Milos Gravara","Juan Luis Herrera","Stefan Nastic"],"abstract":"Low Earth Orbit satellites are pivotal for Earth observation tasks such as wildfire detection, capturing imagery for analysis by Deep Neural Networks (DNNs). On-board analysis reduces downlink volume, but severe energy constraints force a trade-off between computation and data transmission. Splitting the DNN between satellite and ground turns this balance into a design choice, where the split point determines combined computation and downlink energy. Existing approaches typically rely on static split-point decisions that fail to account for dynamic space conditions, or on complex algorithms, historical data, or prediction models that introduce significant overhead. To address these shortcomings, we present STLT, a two-stage system for energy-aware distributed inference between LEO satellites and a ground station. First, an offline analyzer characterizes the DNN architecture to identify a set of favorable split points, consequently reducing the search space. Second, an on-board algorithm performs lightweight energy-aware split selection, dynamically choosing from this pre-computed set based on the current state of the environment. We evaluate STLT on a real-world satellite telemetry dataset, showing that it reduces the split-candidate search space by 30.7% on average, lowers decision latency by 25.1% and energy by 27.5% on average, and delivers up to 20.1% more inference tasks than a state-of-the-art satellite-ground co-inference baseline while consuming 8.8% less energy overall.","published":"2026-10-31","addedAt":"2026-10-31","year":2026,"venue":{"id":"other","label":"Published","year":2026,"isWorkshop":false,"raw":null},"intersection":"ai","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[],"missions":[],"indexedAs":{"arxiv":["cs.DC","cs.AI"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2611.01233","arxiv":"https://arxiv.org/abs/2611.01233","pdf":"https://arxiv.org/pdf/2611.01233","html":"https://arxiv.org/html/2611.01233","doi":"https://doi.org/10.1145/3801927.3858632","code":null,"project":null}},{"id":"2610.41639","title":"Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning","authors":["Chloé Carer","Adam Abdin"],"abstract":"Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing. Planning close-proximity observation viewpoints involves a complex combinatorial problem constrained by coverage, fuel, and safety considerations. This paper models the viewpoint selection as a Constrained Markov Decision Process (CMDP) and applies Proximal Policy Optimization with Lagrangian relaxation to solve it. The system combines a high-level planner, which sequences camera viewpoints on an observation sphere around the target, with a low-level model that assesses each transfer using the Clohessy--Wiltshire (CW) equations for relative motion. The deterministic constraints on single-impulse reachability and $\\Delta v$ cap are enforced directly on the action set. Safety margins are affected by navigation and manoeuvre errors, making the actual clearance stochastic. To address this, a chance constraint on the realised trajectory is implemented, with a Lagrangian multiplier governed by PID control to adapt the penalty during training. The approach is tested on a Galileo-class navigation satellite in medium Earth orbit, modelled as a point cloud where visibility from each viewpoint considers field-of-view and line-of-sight occlusion. Across various risk budgets, the trained policy maintains the keep-out violation rate within the specified limit, achieving coverage close to its maximum and staying within the mission $\\Delta v$ budget. Compared to the usual practice of applying a fixed margin to the keep-out radius and planning deterministically, the chance-constrained policy attains less than half the violation rate at comparable coverage. This makes the collision risk an explicit design parameter, eliminating the need for re-tuning per case. The learned policy selects a viewpoint without the repeated Monte Carlo risk estimates required by a greedy, chance-constrained planner at every decision point.","published":"2026-10-30","addedAt":"2026-10-30","year":2026,"venue":{"id":"iac","label":"IAC 2026","year":2026,"isWorkshop":false,"raw":"77th International Astronautical Congress (IAC), International Astronautical Federation (IAF), Oct 2026, Antalya (TR), Turkey"},"intersection":"both","domains":[{"id":"in-space-servicing","label":"IN-SPACE SERVICING"},{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["arxiv:math.OC"]},{"id":"learning","code":"T8","label":"Learning-based Methods","source":"text","via":["title:reinforcement learning"]}],"missions":[],"indexedAs":{"arxiv":["cs.AI","math.OC"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // IN-SPACE SERVICING","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.41639","arxiv":"https://arxiv.org/abs/2610.41639","pdf":"https://arxiv.org/pdf/2610.41639","html":"https://arxiv.org/html/2610.41639","doi":null,"code":null,"project":null}},{"id":"2610.41647","title":"Risk-Sensitive Mission Planning for Multi-Target On-Orbit Inspection via Distributional Reinforcement Learning","authors":["Adam Abdin"],"abstract":"Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome variability inherent to safety-critical spaceflight. This paper presents an adaptive planning approach for multi-target servicing campaigns in low Earth orbit and applies it to inspection missions flown by a low-thrust servicer. The approach is based on Distributional Reinforcement Learning, which learns the full distribution of mission returns and thereby supports explicit risk-sensitive decision-making at each planning step. Specifically, a Quantile-Regression Deep Q-Network (QR-DQN) agent selects which target to inspect next by jointly considering the expectation and the variance of the predicted return through a mean-variance criterion. The mission environment models circular orbits with a spread in inclination and uses Edelbaum's combined-manoeuvre low-thrust transfer estimates. The observation comprises the orbital elements, the visit history, the observed risk state of each target and the remaining mission-time budget. The risk state of each target changes stochastically during the campaign, making the outcome of an inspection uncertain and creating operationally meaningful distinctions between risk-neutral and risk-averse planning strategies. Because the mission-time budget generally admits only a subset of the candidate targets, the planner must also select which targets to inspect. Numerical experiments on scenarios with five target satellites show that one risk-aversion weight moves the policy across a range of mean return and return variance, over an unchanged environment and reward. Risk-averse agents reduce the variance of the episodic return by approximately one half relative to risk-neutral agents and lower the probability of a mission loss, at a cost in mean return. To validate the learned policies, the efficient frontier, which gives the highest mean return attainable at each level of return variance, is computed exactly by enumerating the admissible visit orders. At equal variance, the learned policies lie close to this frontier in mean return, and the risk-averse policies have a lower probability of a mission loss than the frontier policies.","published":"2026-10-30","addedAt":"2026-10-30","year":2026,"venue":{"id":"iac","label":"IAC 2026","year":2026,"isWorkshop":false,"raw":"77th International Astronautical Congress (IAC), International Astronautical Federation (IAF), Oct 2026, Antalya (Turkey), Turkey"},"intersection":"ai","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"},{"id":"in-space-servicing","label":"IN-SPACE SERVICING"}],"techniques":[{"id":"autonomy-ops","code":"T10","label":"Autonomy, Task Planning & Operations","source":"text","via":["arxiv:cs.AI","title:mission planning"]},{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["arxiv:math.OC"]},{"id":"learning","code":"T8","label":"Learning-based Methods","source":"text","via":["title:reinforcement learning"]}],"missions":[],"indexedAs":{"arxiv":["cs.AI","math.OC"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // SPACE AI & AUTONOMY","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.41647","arxiv":"https://arxiv.org/abs/2610.41647","pdf":"https://arxiv.org/pdf/2610.41647","html":"https://arxiv.org/html/2610.41647","doi":null,"code":null,"project":null}},{"id":"2610.41839","title":"Integrating Low-Thrust Transfer Dynamics into POMDP Planning for On-Orbit Servicing","authors":["Susmitha Patnala","Vincent Mousseau","Adam Abdin"],"abstract":"Recent developments in both high-thrust (HT) and low-thrust (LT) propulsion technologies broaden design options for on-orbit servicing (OOS) missions. However, incorporating LT transfers into OOS mission planning requires accounting for the trade-off between prescribed transfer duration and propellant consumption. Multi-servicing, multi-transfer OOS mission planning is inherently sequential and requires adaptive planning tools that account for propulsion-dependent transfer durations and mass-dependent propellant consumption. Thus, transfer dynamics must be integrated into mission planning and scheduling to increase flexibility and adaptability. In addition, the planning is often done with imperfect information about the system's state and stochastic service demands. This paper focuses on modeling orbital transfers, particularly LT transfers, within a Partially Observable Markov Decision Process (POMDP) framework for OOS mission planning. To enable offline policy computation, the transfer model must provide explicit state transitions and rewards for all admissible actions. The state space is therefore augmented with the servicing vehicle's remaining propellant to enable consistent mass updates. HT transfers are modeled using a precomputed transfer matrix that maps each transfer to the time of flight and propellant consumed. LT transfers are represented using an analytical GEO phasing model with prescribed transfer-duration alternatives. For each alternative, the model calculates propellant consumption and maneuver feasibility as functions of the required phase change and the current servicing-vehicle mass. The proposed modeling approach is evaluated on simplified OOS planning scenarios using circular phasing with HT and LT propulsion. The resulting POMDPs are solved using the Successive Approximations of the Reachable Space under Optimal Policies (SARSOP) algorithm, and the policies obtained under HT and LT are compared. This comparison provides a quantitative assessment of the feasibility and operational impact of LT options, particularly regarding the potential role of advanced electric propulsion in future servicing architectures.","published":"2026-10-30","addedAt":"2026-10-30","year":2026,"venue":{"id":"iac","label":"IAC 2026","year":2026,"isWorkshop":false,"raw":"77th International Astronautical Congress (IAC), Oct 2026, Antalya (Turkey), Turkey"},"intersection":"robotics","domains":[{"id":"in-space-servicing","label":"IN-SPACE SERVICING"},{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["arxiv:math.OC"]},{"id":"autonomy-ops","code":"T10","label":"Autonomy, Task Planning & Operations","source":"text","via":["abstract:mission planning","abstract:scheduling"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO","math.OC"],"openalexTopics":[],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // IN-SPACE SERVICING","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.41839","arxiv":"https://arxiv.org/abs/2610.41839","pdf":"https://arxiv.org/pdf/2610.41839","html":"https://arxiv.org/html/2610.41839","doi":null,"code":null,"project":null}},{"id":"2610.39689","title":"AI for Science in Space: Orbital Laboratories, Planetary Fieldwork, and Embodied Autonomy","authors":["Zizhe Wang","Jia Deng","Xin Lu","Zehao Jia","Yi Huang","Yao Zhu","Jianing Li"],"abstract":"Artificial intelligence for science (AI4S) is extending from interpretation of existing data to the design and execution of experiments. Space offers distinctive scientific opportunities while restricting physical support. Microgravity and extraterrestrial environments expose phenomena that terrestrial experiments cannot fully reproduce; finite samples, constrained resources, and limited opportunities for intervention complicate sustained investigation. This critical review examines orbital laboratories and planetary in situ science across biology, materials, fluid and thermal physics, and adaptive exploration. We distinguish learning from space-derived data, selecting scientific actions, executing them, and preserving experimental validity. Terrestrial self-driving laboratories provide complementary evidence on optimization, dedicated automation, robotic manipulation, and the support needed for unattended operation. The synthesis connects scientific gains already demonstrated by bounded AI systems to gaps in observation, executable actions, and continuity of valid evidence. Space robots can expand the attainable scope of autonomous science when necessary interventions exceed validated instrument capabilities and timely human support is unavailable. Their value must be established against dedicated mechanisms, redundancy, and human-supported operation. We develop a framework connecting sample history, scientific decisions, physical execution, and claim-specific validity. A worked orbital design, a planetary extension, and matched evaluation schemes separate the contributions of sensing, decision policies, and physical actions. The resulting agenda measures autonomy through valid scientific return, task coverage, intervention costs, and evidence integrity across complete campaigns.","published":"2026-10-29","addedAt":"2026-10-29","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"both","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[{"id":"T12717","name":"Space exploration and regulation","score":0.226},{"id":"T12788","name":"Space Science and Extraterrestrial Life","score":0.148},{"id":"T12449","name":"Spacecraft Design and Technology","score":0.134}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.39689","arxiv":"https://arxiv.org/abs/2610.39689","pdf":"https://arxiv.org/pdf/2610.39689","html":"https://arxiv.org/html/2610.39689","doi":null,"code":null,"project":null}},{"id":"2610.39998","title":"Gaze-Assisted Supervisory Control for Lunar Robotic Arm Teleoperation Under Concurrent Monitoring","authors":["Jiayu He","Yifan Li","Oliver R. Runswick","Ernest N. Kamavuako","Yunjie Jia","Peter D. Hodkinson","Yang Gao"],"abstract":"Rover-mounted robotic arms can extend human reach during lunar sample handling, but continuous joystick teleoperation requires repeated visual-motor alignment while operators may also need to monitor system states. This paper presents a gaze-assisted supervisory control framework in which gaze nominates a rock or placement target, explicit confirmation authorizes the action, and the robot performs bounded autonomous grasp-and-place execution. The framework was evaluated against manual joystick teleoperation in a mission-analog virtual reality study with 16 participants. The study used a 2 x 2 x 3 within-subject design varying control mode, placement difficulty, and concurrent monitoring load. Mean recorded task completion time decreased from 47.65 to 18.43 s per rock, with 72.5% of this difference arising during grasping. Under the tested auditory monitoring conditions, gaze-assisted supervisory control showed less average proportional slowing, with no detected control-mode difference in monitoring-response accuracy or speed. The rate of gaze shifts between core areas of interest decreased from 49.98 to 27.73 shifts/min, while the raw NASA Task Load Index score decreased from 32.54 to 18.26. However, the benefit was task dependent. In precision placement, manual control yielded lower mean placement error and a higher placement success rate. These findings support a mixed-initiative allocation in which gaze enables rapid task-level intention specification and autonomous execution, while precision-critical stages retain manual refinement.","published":"2026-10-29","addedAt":"2026-10-29","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"robotics","domains":[],"techniques":[{"id":"hri-teleoperation","code":"T11","label":"HRI & Teleoperation","source":"official","via":["openalex-topic:Teleoperation and Haptic Systems","openalex-topic:Human-Automation Interaction and Safety","openalex-keyword:virtual reality simulation","title:teleoperat"]},{"id":"manipulation","code":"T1","label":"Manipulation & Grasping","source":"text","via":["title:robotic arm","abstract:robotic arm","abstract:sample handling"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[{"id":"T11687","name":"Teleoperation and Haptic Systems","score":0.906},{"id":"T10525","name":"Human-Automation Interaction and Safety","score":0.051},{"id":"T11707","name":"Gaze Tracking and Assistive Technology","score":0.007}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // HUMAN-ROBOT TEAMING","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.39998","arxiv":"https://arxiv.org/abs/2610.39998","pdf":"https://arxiv.org/pdf/2610.39998","html":"https://arxiv.org/html/2610.39998","doi":null,"code":null,"project":null}},{"id":"2610.38141","title":"SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites","authors":["Hogyun Kim","Juwon Kim","Dongjin Cho","Jiwon Choi","Geonmo Yang","Seokhwan Jeong","Jiyun Lee","Miryeong Park","Hyungtae Lim","Younggun Cho"],"abstract":"Future planetary exploration missions require teams of robots to collaboratively map unknown environments under severe communication limits, constrained onboard computation, and no central infrastructure. In this field report, we present SKiD-SLAM, a distributed multi-robot LiDAR simultaneous localization and mapping (SLAM) system for such environments, and report its deployment across two mission concepts: cave exploration and scientific surface exploration. We survey the communication and computation budgets of actual planetary missions and select modules that fit these budgets for inter-robot place recognition, relative pose estimation, and distributed trajectory fusion. Before field deployment, we verify the assembled system on public datasets resembling planetary environments and in a simulator built on NASA JPL's Open Source Rover. We then validate SKiD-SLAM through field deployments in South Korea: a team of wheeled rovers explores the Majang lava-tube cave on Jeju Island, and quadruped robots carry out a crater-and-rock science scenario on a planetary analog terrain at the Korea Institute of Civil Engineering and Building Technology (KICT) in Yeoncheon. Across both deployments, SKiD-SLAM reliably performed distributed multi-robot LiDAR SLAM within a 250 kbps inter-robot communication budget, demonstrating its feasibility under challenging field conditions. Supplementary materials, including code and field data, are available at https://sparolab.github.io/research/skid_slam/.","published":"2026-10-28","addedAt":"2026-10-28","year":2026,"venue":{"id":"jfr","label":"JFR / Field Robotics 2026","year":2026,"isWorkshop":false,"raw":"IEEE transactions on field robotics."},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"perception","code":"T5","label":"Perception & State Estimation","source":"official","via":["openalex-topic:Robotics and Sensor-Based Localization","openalex-keyword:multi-robot SLAM","openalex-keyword:LiDAR SLAM","openalex-keyword:distributed SLAM"]},{"id":"multi-robot","code":"T12","label":"Multi-Robot & Swarms","source":"official","via":["openalex-keyword:multi-robot SLAM","title:multi-robot","abstract:multi-robot"]},{"id":"simulation-testbeds","code":"T13","label":"Simulation, Digital Twins & Benchmarks","source":"text","via":["title:analog site","abstract:dataset","abstract:simulator"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[{"id":"T10191","name":"Robotics and Sensor-Based Localization","score":0.999},{"id":"T10586","name":"Robotic Path Planning Algorithms","score":0},{"id":"T10462","name":"Reinforcement Learning in Robotics","score":0}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.38141","arxiv":"https://arxiv.org/abs/2610.38141","pdf":"https://arxiv.org/pdf/2610.38141","html":"https://arxiv.org/html/2610.38141","doi":"https://doi.org/10.1109/tfr.2026.3721725","code":null,"project":"https://sparolab.github.io/research/skid_slam/"}},{"id":"2610.38657","title":"Explore Where It Matters: Targeted Exploration for Efficient Rover Adaptation","authors":["Jacob Levy","Arthur Bouton","Tristan D. Hassler","Hari Nayar","David Fridovich-Keil"],"abstract":"Future planetary rovers will traverse steep, deformable terrain where slip, sinkage, and terrain deformation can threaten mobility. Although simulation can capture some wheel-soil effects, unmodeled deformable-terrain phenomena create sim-to-real gaps requiring deployment-time adaptation. Yet, rover operations leave little room for extensive trial-and-error. We introduce TERRA (Targeted Exploration for Reward-Relevant Adaptation), an online adaptation framework that efficiently seeks deployment data exposing task-relevant model errors and corrects them. TERRA adapts a simulation-trained world model with a reward-weighted prediction loss and trains a loss predictor to estimate where future performance-relevant errors are likely to occur, using this signal to guide exploration during MPC. In simulation, TERRA reaches approximately 90% slope-climbing success within five trials under severe out-of-distribution pretraining conditions, outperforming exploration baselines in reward, completion time, and energy to first success. On the ERNEST rover, TERRA recovers coordinated active-suspension locomotion on a deformable Mars-simulant slope within three to five real-world trials. Project website: https://terra-explore.github.io","published":"2026-10-28","addedAt":"2026-10-28","year":2026,"venue":{"id":"corl","label":"CoRL 2026","year":2026,"isWorkshop":false,"raw":null},"intersection":"both","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"surface-mobility","code":"T2","label":"Surface Mobility & Terramechanics","source":"official","via":["openalex-topic:Soil Mechanics and Vehicle Dynamics","openalex-keyword:wheel-soil interaction","openalex-keyword:slope climbing","abstract:slip"]},{"id":"learning","code":"T8","label":"Learning-based Methods","source":"official","via":["arxiv:cs.LG","openalex-topic:Reinforcement Learning in Robotics","openalex-keyword:sim-to-real gap"]},{"id":"perception","code":"T5","label":"Perception & State Estimation","source":"official","via":["openalex-topic:Robotics and Sensor-Based Localization"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO","cs.AI","cs.LG"],"openalexTopics":[{"id":"T10462","name":"Reinforcement Learning in Robotics","score":0.995},{"id":"T11978","name":"Soil Mechanics and Vehicle Dynamics","score":0.004},{"id":"T10191","name":"Robotics and Sensor-Based Localization","score":0.001}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.38657","arxiv":"https://arxiv.org/abs/2610.38657","pdf":"https://arxiv.org/pdf/2610.38657","html":"https://arxiv.org/html/2610.38657","doi":null,"code":null,"project":null}},{"id":"2610.36625","title":"Polarity--Time Interaction and Multi-Level Reliability for Event-Based Spacecraft Pose Estimation","authors":["Yonglong Zhang","Zongwu Xie","and Yang Liu"],"abstract":"Sparse event responses produce uneven keypoint errors whose effects on spacecraft pose depend on correspondence geometry and the reliability of successive observations. We present a reliability-aware framework connecting polarity--time localization, geometric recovery, event--CAD refinement, and trajectory optimization. A three-branch representation combines positive events, negative events, and recent-event timing through cross-branch attention and gating, followed by spatiotemporal aggregation. An ordered semantic-point head estimates relative correspondence precision for geometrically constrained pose recovery. Event-edge and CAD-projection agreement then guide local pose refinement under keypoint-geometry safeguards. At the sequence level, a ten-dimensional descriptor combines heatmap shape, reprojection consistency, and geometric conditioning to set time-aligned observation scales. On the official SPADES challenge test set, the complete system achieves a combined pose score of 0.0474, approximately 5.4\\% below PASAN+IRIS. Validation ablations reduce the score from 0.0772 for base localization with uniform PnP to 0.0514 for the complete cascade; on identical single-frame observations, quality weighting reduces the score from 0.0549 with constant-weight optimization to 0.0514.","published":"2026-10-27","addedAt":"2026-10-27","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"robotics","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"}],"techniques":[{"id":"perception","code":"T5","label":"Perception & State Estimation","source":"official","via":["openalex-topic:Advanced Vision and Imaging","openalex-keyword:spacecraft pose estimation","openalex-keyword:event cameras","title:pose estimation"]},{"id":"learning","code":"T8","label":"Learning-based Methods","source":"official","via":["openalex-topic:Advanced Neural Network Applications"]}],"missions":[],"indexedAs":{"arxiv":["cs.AI","cs.RO"],"openalexTopics":[{"id":"T11701","name":"Space Satellite Systems and Control","score":0.804},{"id":"T10036","name":"Advanced Neural Network Applications","score":0.04},{"id":"T10531","name":"Advanced Vision and Imaging","score":0.026}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.36625","arxiv":"https://arxiv.org/abs/2610.36625","pdf":"https://arxiv.org/pdf/2610.36625","html":"https://arxiv.org/html/2610.36625","doi":null,"code":null,"project":null}},{"id":"2610.37043","title":"State estimation for a hybrid rigid-continuum space manipulator system","authors":["Lorenzo Ticozzi","Patricio A. Vela","and Panagiotis Tsiotras"],"abstract":"Spacecraft-mounted robotic manipulators can support long-term operability of orbiting assets by conducting tasks such as on-orbit assembly, repair, upgrade, and refueling. In this context, the integration of continuum/soft robotic mechanisms into space manipulator designs promises to reduce stowage volume and mass, as well as to improve operational safety through the inherent mechanical compliance of soft structures. This work presents a novel state reconstruction pipeline for a hybrid soft-rigid space manipulator, in which the distal link is a tendon-driven continuum robot. The estimation problem is formulated as a maximum a posteriori (MAP) inference problem over the hybrid manipulator's configuration, conditioned on discrete, noisy measurements from joint encoders and a vision-based sensing pipeline. The resulting soft-constrained optimization problem is transcribed as a factor graph, providing a compact representation for fusing heterogeneous measurements with the rigid-soft kinematic model. Furthermore, the adoption of the Geometric Variable Strain (GVS) approach yields a minimum-order parameterization of the configuration, a property expected to facilitate model-based feedback control design in future work. The proposed implementation is validated in simulation on a closed-loop trajectory where, for better realism, position measurements for the continuum link are obtained by means of a simulated visual pipeline leveraging synthetic photorealistic images. The state reconstruction accuracy and computational performance confirm the potential of the proposed framework for hybrid soft-rigid robotic applications in orbit.","published":"2026-10-27","addedAt":"2026-10-27","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"robotics","domains":[{"id":"orbital-robotics","label":"ORBITAL ROBOTICS"},{"id":"in-space-servicing","label":"IN-SPACE SERVICING"}],"techniques":[{"id":"manipulation","code":"T1","label":"Manipulation & Grasping","source":"official","via":["openalex-keyword:space manipulators","title:space manipulator","abstract:manipulat","abstract:space manipulator"]},{"id":"perception","code":"T5","label":"Perception & State Estimation","source":"official","via":["openalex-keyword:state estimation","title:state estimation"]},{"id":"design-hardware","code":"T14","label":"Design, Mechanisms & Hardware","source":"official","via":["openalex-topic:Soft Robotics and Applications","abstract:soft robot","abstract:tendon"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO"],"openalexTopics":[{"id":"T10868","name":"Soft Robotics and Applications","score":0.94},{"id":"T11701","name":"Space Satellite Systems and Control","score":0.027},{"id":"T10571","name":"Robotic Mechanisms and Dynamics","score":0.021}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // ORBITAL ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.37043","arxiv":"https://arxiv.org/abs/2610.37043","pdf":"https://arxiv.org/pdf/2610.37043","html":"https://arxiv.org/html/2610.37043","doi":null,"code":null,"project":null}},{"id":"2610.37199","title":"A Bi-level Co-design Framework for Lunar Monoped with 2D Jump and Landing Control","authors":["Sanjay Ramkumar","Suraj Kumar"],"abstract":"Legged robots for lunar exploration must be designed for low gravity, where reduced contact forces and long flight phases make leg morphology and controller choice interdependent. We study this for a planar monoped in lunar gravity ($1.625$ m/s$^2$) using a bi-level co-design framework: a differential evolution outer loop searches over link lengths, and each candidate is evaluated on a vertical jump by either a quadratic program based task-space controller (QP-TSC) or a direct-collocation based trajectory optimization. The two controllers yield different optimal link lengths, and trajectory optimization outperforms the QP-TSC across most of the design space, with 17% lower Cost of Transport (COT) at their respective optima. We then extend to a 2D jump, where trajectory optimization with centroidal angular momentum regulation produces stable in-place and forward jumps, and an LQR controller absorbs impact and stabilizes the robot after touchdown. This is demonstrated on a baseline morphology with both links 0.3 m long.","published":"2026-10-27","addedAt":"2026-10-27","year":2026,"venue":{"id":"preprint","label":"Preprint","year":null,"isWorkshop":false,"raw":null},"intersection":"robotics","domains":[{"id":"planetary-robotics","label":"PLANETARY ROBOTICS"}],"techniques":[{"id":"gnc","code":"T7","label":"Guidance, Dynamics & Control","source":"official","via":["arxiv:eess.SY","openalex-keyword:trajectory optimization","abstract:trajectory optimization"]},{"id":"surface-mobility","code":"T2","label":"Surface Mobility & Terramechanics","source":"official","via":["openalex-topic:Robotic Locomotion and Control","openalex-keyword:legged robots"]}],"missions":[],"indexedAs":{"arxiv":["cs.RO","cs.SY","eess.SY"],"openalexTopics":[{"id":"T10879","name":"Robotic Locomotion and Control","score":1},{"id":"T10571","name":"Robotic Mechanisms and Dynamics","score":0},{"id":"T11023","name":"Prosthetics and Rehabilitation Robotics","score":0}],"ntrsSubjects":[],"ieeeTerms":[]},"cardTag":"RESEARCH // PLANETARY ROBOTICS","citations":0,"links":{"page":"https://space-robots.org/papers/paper/2610.37199","arxiv":"https://arxiv.org/abs/2610.37199","pdf":"https://arxiv.org/pdf/2610.37199","html":"https://arxiv.org/html/2610.37199","doi":null,"code":null,"project":null}}]}