An End-to-End Differentiable Framework for Contact-Aware Robot Design
- URL: http://arxiv.org/abs/2107.07501v1
- Date: Thu, 15 Jul 2021 17:53:44 GMT
- Title: An End-to-End Differentiable Framework for Contact-Aware Robot Design
- Authors: Jie Xu, Tao Chen, Lara Zlokapa, Michael Foshey, Wojciech Matusik,
Shinjiro Sueda, Pulkit Agrawal
- Abstract summary: We build an end-to-end differentiable framework for contact-aware robot design.
A novel deformation-based parameterization allows for the design of articulated rigid robots with arbitrary, complex geometry.
A differentiable rigid body simulator can handle contact-rich scenarios and computes analytical gradients for a full spectrum of kinematic and dynamic parameters.
- Score: 37.715596272425316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
contact-aware 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.
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