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About this paper
Zhefei Gong, Shangke Lyu, Pengxiang Ding, Wei Xiao, Donglin Wang
Researchers from Westlake University, Nanjing University, and Zhejiang University developed KORR (Koopman-guided Online Residual Refinement), a framework that integrates Koopman operator theory into residual policy learning for robotic manipulation. This approach consistently improved success rates and robustness across challenging long-horizon assembly tasks by modeling global system dynamics.
Key insights
- The enforced linear time-invariant structure of Koopman dynamics in the lifted latent space provides superior stability and extrapolation capabilities for residual refinement compared to learned non-linear dynamics.
- Conditioning the residual policy on Koopman-imagined future latent states enables the policy to generate globally informed and stable action refinements, overcoming the limitations of local corrections.
- Backpropagating the reinforcement learning loss through the Koopman dynamics module is crucial for learning task-relevant lifted representations, ensuring the dynamics model captures reward-aligned features.
Summary by alphaXiv.
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