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.
Multi-expert learning of adaptive legged locomotion.Science Robotics, 5(49):eabb2174
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CMP projects actions onto a learned competence manifold using a frame-wise safety scheme and isomorphic latent space to achieve up to 10x better survival in out-of-distribution scenarios with under 10% tracking loss.
MoE-based locomotion policy with RoboGauge metrics achieves reliable sim-to-real transfer, enabling robust quadrupedal walking on challenging unseen terrains up to 4 m/s.
A factorized modular diffusion policy improves fitting of multimodal robot actions and enables flexible task adaptation without catastrophic forgetting.
citing papers explorer
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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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CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
CMP projects actions onto a learned competence manifold using a frame-wise safety scheme and isomorphic latent space to achieve up to 10x better survival in out-of-distribution scenarios with under 10% tracking loss.
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Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion
MoE-based locomotion policy with RoboGauge metrics achieves reliable sim-to-real transfer, enabling robust quadrupedal walking on challenging unseen terrains up to 4 m/s.
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Flexible Multitask Learning with Factorized Diffusion Policy
A factorized modular diffusion policy improves fitting of multimodal robot actions and enables flexible task adaptation without catastrophic forgetting.