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

2024-10-27

IEEE Robotics and Automation Letters

AI Summary

To address degraded control accuracy in sim-to-real transfer caused by dynamical mismatches, this paper proposes a gradient-free in-context learning method: leveraging the robot’s historical interaction sequence as dynamic prompts to adaptively tune simulation parameters online for rapid and precise system identification. This work is the first to introduce in-context learning into sim-to-real system identification, integrating a Transformer architecture, a gradient-free online adaptation mechanism, and dynamics-deviation-aware prompt encoding—thereby eliminating reliance on domain randomization and differentiable optimization. Evaluated on object scooping and tabletop pneumatic air-hockey tasks, the method reduces simulation parameter estimation error by 80% and 42%, respectively, and achieves ≥70% scooping success across three distinct object categories.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Manipulation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 match 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.

Problem

• Addresses sim-to-real transfer challenges in robotics.
• Proposes in-context learning for dynamic simulation adjustments.
• Improves alignment between simulated and real-world dynamics.

Innovation

• Dynamic simulation adjustment using in-context learning
• Leverages past interaction histories for real-world adaptation
• Achieves high success rates in sim-to-real transfer tasks