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

Shiqi Liu, Xilun Zhang, Peide Huang, William Han, Yiqi Lyu, Mengdi Xu, Ding Zhao

Keywords: In-context Learning, Sim-to-Real Transfer, Robotics

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. Using 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.

Submission Number: 5