Explicit conditioning of a PPO policy on interpretable stair parameters (height, depth, yaw) yields improved generalization to unseen stairs and reliable real-world traversal on the Unitree G1, including 33 consecutive outdoor steps.
Robust reinforcement learning.Neural computation, 17(2):335–359
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Agentic safety fails to generalize across tasks because the task-to-safe-controller mapping has a higher Lipschitz constant than the task-to-controller mapping alone, as proven in linear-quadratic control and demonstrated in quadcopter and LLM experiments.
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Explicit Stair Geometry Conditioning for Robust Humanoid Locomotion
Explicit conditioning of a PPO policy on interpretable stair parameters (height, depth, yaw) yields improved generalization to unseen stairs and reliable real-world traversal on the Unitree G1, including 33 consecutive outdoor steps.
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Why Does Agentic Safety Fail to Generalize Across Tasks?
Agentic safety fails to generalize across tasks because the task-to-safe-controller mapping has a higher Lipschitz constant than the task-to-controller mapping alone, as proven in linear-quadratic control and demonstrated in quadcopter and LLM experiments.