[2603.22039] RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning

Computer Science > Robotics arXiv:2603.22039 (cs) [Submitted on 23 Mar 2026] Title:RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning Authors:Dong Heon Cho, Boyuan Chen View a PDF of the paper titled RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning, by Dong Heon Cho and Boyuan Chen View PDF HTML (experimental) 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...