Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers

• URL: http://arxiv.org/abs/2410.07584v2
• Date: Tue, 25 Mar 2025 13:23:21 GMT
• Title: Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers
• Authors: Jianxin Bi, Kelvin Lim, Kaiqi Chen, Yifei Huang, Harold Soh,
• Abstract summary: We propose a plan-then-control framework aimed at improving the action-data efficiency of inverse dynamics controllers.<n>Specifically, we adopt a Deep Koopman Operator framework to model the dynamical system and utilize observation-only trajectories to learn a latent action representation.<n>This latent representation can then be effectively mapped to real high-dimensional continuous actions using a linear action decoder.
• Score: 23.292429025366417
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in diffusion-based robot policies have demonstrated significant potential in imitating multi-modal behaviors. However, these approaches typically require large quantities of demonstration data paired with corresponding robot action labels, creating a substantial data collection burden. In this work, we propose a plan-then-control framework aimed at improving the action-data efficiency of inverse dynamics controllers by leveraging observational demonstration data. Specifically, we adopt a Deep Koopman Operator framework to model the dynamical system and utilize observation-only trajectories to learn a latent action representation. This latent representation can then be effectively mapped to real high-dimensional continuous actions using a linear action decoder, requiring minimal action-labeled data. Through experiments on simulated robot manipulation tasks and a real robot experiment with multi-modal expert demonstrations, we demonstrate that our approach significantly enhances action-data efficiency and achieves high task success rates with limited action data.

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