用历史数据动态调整仿真参数，提升机器人真实世界表现
Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications
- 基于交互历史实时调整仿真环境参数，无需梯度更新
- 在物体刮取和桌式冰球任务中，参数估计误差降低42%~80%
- 三类物体上实现超70%的仿真到现实迁移成功率
由于仿真与真实世界动力学存在差异，仿真到现实的迁移仍是机器人领域的重大挑战。传统方法如域随机化难以捕捉精细动力学，限制了其在精确控制任务中的应用。本文提出一种新方法，通过在线使用上下文学习动态调整仿真环境参数。利用过往交互历史作为上下文，该方法在不依赖梯度更新的前提下，使仿真动力学快速准确地匹配真实动力学，显著提升仿真与现实性能的一致性。我们在物体刮取和桌式冰球两个任务上验证该方法：在仿真-仿真评估中，参数估计精度相比基线分别提升80%和42%；在仿真-现实迁移中，物体刮取任务对三种不同物体均达到至少70%的成功率。通过融合历史交互数据，本方法实现了高效且平稳的系统辨识，推动机器人在动态真实场景中的部署。演示视频见项目主页：https://sim2real-capture.github.io/
原文摘要 · Abstract (English)
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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