Regularized Deep Signed Distance Fields for Reactive Motion Generation
- URL: http://arxiv.org/abs/2203.04739v1
- Date: Wed, 9 Mar 2022 14:21:32 GMT
- Title: Regularized Deep Signed Distance Fields for Reactive Motion Generation
- Authors: Puze Liu, Kuo Zhang, Davide Tateo, Snehal Jauhri, Jan Peters and
Georgia Chalvatzaki
- Abstract summary: Distance-based constraints are fundamental for enabling robots to plan their actions and act safely.
We propose Regularized Deep Signed Distance Fields (ReDSDF), a single neural implicit function that can compute smooth distance fields at any scale.
We demonstrate the effectiveness of our approach in representative simulated tasks for whole-body control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces.
- Score: 30.792481441975585
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous robots should operate in real-world dynamic environments and
collaborate with humans in tight spaces. A key component for allowing robots to
leave structured lab and manufacturing settings is their ability to evaluate
online and real-time collisions with the world around them. Distance-based
constraints are fundamental for enabling robots to plan their actions and act
safely, protecting both humans and their hardware. However, different
applications require different distance resolutions, leading to various
heuristic approaches for measuring distance fields w.r.t. obstacles, which are
computationally expensive and hinder their application in dynamic obstacle
avoidance use-cases. We propose Regularized Deep Signed Distance Fields
(ReDSDF), a single neural implicit function that can compute smooth distance
fields at any scale, with fine-grained resolution over high-dimensional
manifolds and articulated bodies like humans, thanks to our effective data
generation and a simple inductive bias during training. We demonstrate the
effectiveness of our approach in representative simulated tasks for whole-body
control (WBC) and safe Human-Robot Interaction (HRI) in shared workspaces.
Finally, we provide proof of concept of a real-world application in a HRI
handover task with a mobile manipulator robot.
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