Differentiable Simulation of Soft Multi-body Systems
This work addresses the challenge of efficient and realistic simulation for soft robots, enabling improved motion control learning, though it appears incremental in building on existing frameworks like Projective Dynamics.
The paper tackles the problem of simulating soft articulated bodies by developing a differentiable simulation method that integrates physical dynamics into gradient-based pipelines, resulting in more stable and realistic simulations and accelerating system identification by over an order of magnitude.
We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynamics and derive a generalized dry friction model for soft continuum using a new matrix splitting strategy. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes. The experiments demonstrate that our designs make soft body simulation more stable and realistic compared to other frameworks. Our method accelerates the solution of system identification problems by more than an order of magnitude, and enables efficient gradient-based learning of motion control with soft robots.