Director of Research (if dissertation) or Advisor (if thesis)
Wang, Shenlong
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Robotics
Imitation Learning
Diffusion Policy
Neural Dynamics
Robotic Manipulation
Language
eng
Abstract
Imitation learning has been proven effective in mimicking demonstrations across various robotic manipulation tasks. However, to develop robust policies, current imitation methods, such as diffusion policy, require training on extensive demonstrations, making data collection labor-intensive. In contrast, model-based planning with dynamics models can effectively cover a sufficient range of configurations using only off-policy data. Yet, without the guidance of expert demonstrations, many tasks are difficult and time-consuming to plan using the dynamics models. Therefore, we take the best of both model learning and imitation learning, and propose neural dynamics augmented imitation learning that covers large-scene configurations with few-shot demonstrations. This method trains a robust diffusion policy in a local support region using few-shot demonstrations and rearranges objects outside this region into it using offline-trained neural dynamics models. Extensive experiments across various tasks in both simulations and real-world scenarios, including granular manipulation, contact-rich tasks, and multi-object interaction tasks, have demonstrated that trained with only 1 to 30 demonstrations, our proposed method can robustly cover a significantly larger area than the policy trained purely from the demonstrations.
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