An End-to-End Differentiable Framework for Contact-Aware Robot Design
Year: 2021
Type: preprint
Abstract: The current dominant paradigm for robotic manipulation involves two separate stages: manipulator design and control.Because the robot's morphology and how it can be controlled are intimately linked, joint optimization of design and control can significantly improve performance.Existing methods for co-optimization are limited and fail to explore a rich space of designs.The primary reason is the trade-off between the complexity of designs that is necessary for contact-rich tasks against the practical constraints of manufacturing, optimization, contact handling, etc.We overcome several of these challenges by building an end-to-end differentiable framework for contactaware robot design.The two key components of this framework are: a novel deformation-based parameterization that allows for the design of articulated rigid robots with arbitrary, complex geometry, and a differentiable rigid body simulator that can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters.On multiple manipulation tasks, our framework outperforms existing methods that either only optimize for control or for design using alternate representations or co-optimize using gradient-free methods.
Institutions: Massachusetts Institute of Technology, Texas A&M University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3181222244