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

• URL: http://arxiv.org/abs/2603.22039v1
• Date: Mon, 23 Mar 2026 14:40:02 GMT
• Title: RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning
• Abstract summary: Differentiable simulators enable gradient-based optimization of soft robots over material parameters, control, and morphology.<n>We propose a residual acceleration field learning framework that augments a base simulator with a transferable, element-level corrective dynamics field.
• Score: 2.9644314918775962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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