End-to-End Reinforcement Learning for Quadrupedal Jumping in Lunar Gravity
We present a multistage reinforcement-learning approach that produces a single end-to-end control policy for commanded point-to-point jumping under lunar gravity.
ProofPaper ↗
Key points
- Legged robots can extend planetary exploration to rugged and discontinuous terrain that is inaccessible to conventional rovers.
- In lunar gravity, jumping further expands this mobility by enabling robots to cross obstacles and gaps that cannot be traversed by walking alone.
- We analyze the torque and power demands of lunar jumping and characterize performance degradation under extreme outlier events, including control-update freezes of up to \SI{1}{s} during flight.
- Hardware transfer demonstrates that the policy can successfully execute a vertical jump under lunar-gravity offloading.
Sources (1)
- [1]End-to-End Reinforcement Learning for Quadrupedal Jumping in Lunar GravityarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 11:27 AM
We present a multistage reinforcement-learning approach that produces a single end-to-end control policy for commanded point-to-point jumping under lunar gravity.
Legged robots can extend planetary exploration to rugged and discontinuous terrain that is inaccessible to conventional rovers.
Extractive summary: sentences quoted from the sources.
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