RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning

2026-03-23

AI Summary

This work addresses the challenge of system identification bias in sim-to-real transfer for soft robotics, which arises from constitutive model misspecification or sparse observations—particularly pronounced when geometric morphology serves as a design variable. To mitigate this, the authors propose the Residual Acceleration Field Learning (RAFL) framework, which augments a base simulator with a local residual dynamics field that is independent of mesh topology and discretization. RAFL leverages locally shared features and is trained end-to-end, enabling zero-shot generalization across morphologies. By integrating differentiable simulation with sparsely labeled real-world observations, RAFL consistently outperforms conventional system identification approaches in both sim-to-sim and sim-to-real experiments, effectively avoiding negative transfer and supporting cumulative improvements in simulation fidelity throughout continuous optimization processes.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Differentiable simulators enable gradient-based optimization of soft robots over material parameters, control, and morphology, but accurately modeling real systems remains challenging due to the sim-to-real gap. This issue becomes more pronounced when geometry is itself a design variable. System identification reduces discrepancies by fitting global material parameters to data; however, when constitutive models are misspecified or observations are sparse, identified parameters often absorb geometry-dependent effects rather than reflect intrinsic material behavior. More expressive constitutive models can improve accuracy but substantially increase computational cost, limiting practicality. We propose a residual acceleration field learning (RAFL) framework that augments a base simulator with a transferable, element-level corrective dynamics field. Operating on shared local features, the model is agnostic to global mesh topology and discretization. Trained end-to-end through a differentiable simulator using sparse marker observations, the learned residual generalizes across shapes. In both sim-to-sim and sim-to-real experiments, our method achieves consistent zero-shot improvements on unseen morphologies, while system identification frequently exhibits negative transfer. The framework also supports continual refinement, enabling simulation accuracy to accumulate during morphology optimization.

Problem

• sim-to-real
• soft robots
• system identification
• constitutive models
• generalization

Innovation

• residual acceleration field learning
• sim-to-real transfer
• differentiable simulation
• soft robotics
• generalizable dynamics correction