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About this paper
Ning Huang, Zhentao Xie, Qinchuan Li
This research introduces Conditional Adversarial Motion Priors (CAMP), a framework enabling quadruped robots to learn and smoothly transition between diverse locomotion skills using a single policy. The approach mitigates mode collapse in adversarial imitation learning and was successfully deployed on physical Unitree Go2 and A1 robots with direct sim-to-real transfer, demonstrating over 91% joint tracking accuracy for learned gaits.
Key insights
- Conditioning the adversarial discriminator on skill categories effectively mitigates mode collapse, ensuring the policy learns and distinguishes diverse behaviors.
- An explicit skill discriminator providing similarity-based rewards enables precise reconstruction of intended skill characteristics and enhances policy expressiveness for multiple skills.
- Combining conditional learning with a dedicated skill discriminator and robust domain randomization allows for stable multi-skill acquisition and direct deployment on physical hardware.
Summary by alphaXiv.
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