Energy-Efficient Learning-Based Control of a Legged Robot in Multiple Gravity Environments

Legged robots are promising candidates for exploring challenging areas on low-gravity bodies such as the Moon, Mars, or asteroids, thanks to their advanced mobility on unstructured terrain. However, as planetary robots’ power and thermal budgets are highly restricted, these robots need energy-efficient control approaches that easily transfer to multiple gravity environments. In this work, we introduce a reinforcement learning-based control approach for legged robots with gravity-scaled power-optimized reward functions. We use our approach to develop and validate a locomotion controller and a base pose controller in gravity environments from lunar gravity ($1.62 \mathrm{~m} / \mathrm{s}^{2}$) to a hypothetical super-Earth $\left(19.62 \mathrm{~m} / \mathrm{s}^{2}\right)$ in simulation. Our approach successfully scales across these gravity levels for locomotion and base pose control with the gravity-scaled reward functions. On the real system, the power-optimized locomotion controller reached a power consumption for locomotion of 23.4 W in Earth gravity on a 15.65 kg robot at $0.4 \mathrm{~m} / \mathrm{s}$, a $23 \%$ improvement over a baseline policy. Additionally, we designed a constant-force spring offload system that allowed us to conduct real-world experiments on legged locomotion in lunar gravity. In this test setup, the power-optimized control policy reached $12.2 \mathrm{~W}, 36 \%$ less than a baseline controller which is not optimized for power efficiency. Our method provides a scalable approach to developing power-efficient locomotion controllers for legged robots across multiple gravity levels.