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Efficient Learning-Based Control of a Legged Robot in Lunar Gravity
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Author / Producer
Date
2025-09-12
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
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 m/s2) to a hypothetical super-Earth (19.62 m/s2). Our approach successfully scales across these gravity levels for locomotion and base pose control with the gravity-scaled reward functions. 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 m/s, a 23 % improvement over the 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 lunar gravity, the power-optimized control policy reached 12.2 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.
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accepted
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Journal / series
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Pages / Article No.
Publisher
Event
iSparo2025
Edition / version
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Date collected
Date created
Subject
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Funding
-- - NCCR Digital Fabrication (SNF)