Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications

• URL: http://arxiv.org/abs/2410.20357v1
• Date: Sun, 27 Oct 2024 07:13:38 GMT
• Title: Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications
• Authors: Xilun Zhang, Shiqi Liu, Peide Huang, William Jongwon Han, Yiqi Lyu, Mengdi Xu, Ding Zhao,
• Abstract summary: We propose a novel approach that dynamically adjusts simulation environment parameters online using in-context learning. We validate our approach across two tasks: object scooping and table air hockey. Our approach delivers efficient and smooth system identification, advancing the deployment of robots in dynamic real-world scenarios.
• Score: 23.94013806312391
• License:
• Abstract: Sim-to-real transfer remains a significant challenge in robotics due to the discrepancies between simulated and real-world dynamics. Traditional methods like Domain Randomization often fail to capture fine-grained dynamics, limiting their effectiveness for precise control tasks. In this work, we propose a novel approach that dynamically adjusts simulation environment parameters online using in-context learning. By leveraging past interaction histories as context, our method adapts the simulation environment dynamics to real-world dynamics without requiring gradient updates, resulting in faster and more accurate alignment between simulated and real-world performance. We validate our approach across two tasks: object scooping and table air hockey. In the sim-to-sim evaluations, our method significantly outperforms the baselines on environment parameter estimation by 80% and 42% in the object scooping and table air hockey setups, respectively. Furthermore, our method achieves at least 70% success rate in sim-to-real transfer on object scooping across three different objects. By incorporating historical interaction data, our approach delivers efficient and smooth system identification, advancing the deployment of robots in dynamic real-world scenarios. Demos are available on our project page: https://sim2real-capture.github.io/

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