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End-to-End Reinforcement Learning for Quadrupedal Jumping in Lunar Gravity

The study proposes a multistage reinforcement learning method that produces a single end-to-end control policy for commanded point-to-point jumping by a quadruped robot under lunar gravity. The policy architecture is simple and relies only on mission-compatible onboard sensing; in simulation, landing errors are smaller than the robot's body size. The work also analyzes torque and power demands and characterizes performance degradation under extreme outlier events, including control-update freezes of up to 1 second during flight. In hardware transfer tests, the policy successfully completed a vertical jump under lunar-gravity offloading, and measured torque and power profiles matched the simulation and stayed within hardware limits.

Part of the event: End-to-End Reinforcement Learning Study of Quadrupedal Robot Jumping in Lunar Gravity

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