Problem
Research questions and friction points this paper is trying to address.
soft robots
modelling
system identification
dynamics
pneumatic
An End-to-End Framework for Modeling Pneumatic Soft Robots Based on Differentiable Finite Element Methods
🤖 AI Summary
This study addresses the challenges of low modeling accuracy and poor computational efficiency in soft robotics arising from strong nonlinearities and complex geometries. To this end, we propose an end-to-end differentiable finite element framework. The method automatically voxelizes CAD models and integrates viscoelastic constitutive laws with point cloud observations, enabling efficient material parameter identification and motion trajectory planning via gradient-based optimization. Experimental results demonstrate that the system achieves a positional error of less than 3 millimeters. Furthermore, its high modeling fidelity is validated through path-following and grasping tasks. Ultimately, this work establishes a novel paradigm for the precise control of soft robots.
📝 Abstract
Soft robots present significant modeling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulties in accurate system identification and dynamics modeling limit their applications in precise robotics tasks. Prior modeling approaches typically suffer from trade-offs in accuracy, computational efficiency, or speed. The differentiable finite element method (FEM) offers a promising balance between these desiderata for soft robot modeling, and enables the use of gradients for efficient calibration and trajectory optimisation. In this paper, we propose an end-to-end differentiable FEM-based framework designed to streamline modeling and system identification for pneumatic soft robots, enabling the generation of reliable models for downstream tasks. The framework automates the conversion of computer-aided designs into voxelised tetrahedral viscoelastic FEM meshes that accurately represent the robot’s geometry and actuation mechanism. By integrating easily acquired point cloud data with differentiable FEM, the framework achieves precise identification of material and dynamic actuation parameters using a minimal experimental setup. Additionally, the differentiable nature of the model facilitates trajectory optimisation by leveraging gradients from robot dynamics and contact interactions. We validate the framework by modeling a complex bellow-shaped pneumatic soft robot and demonstrate its efficacy in real-world motion planning tasks. Experimental results indicate high modeling accuracy, with a maximum positional error of less than 3 mm, and successful application in tasks such as path following and grasping.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Differentiable Finite Element Method
Soft Robot Modeling
End-to-End Framework
System Identification
Trajectory Optimization
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