Abstract
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Learning reachable manifold and inverse mapping for a redundant robot manipulator
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| Seungsu Kim, Julien Perez |
| IEEE International Conference on Robotics and Automation 2021 (ICRA), Xi’an, China, 30 May–5 June 2021 |
Validating the kinematic feasibility of a planned robot motion and finding corresponding inverse solutions are time-consuming processes, especially for long-horizon manipulation tasks. Most existing approaches are based on solving iterative gradient-based optimization, so the processes are time-consuming and have a high risk of falling in local minima. In this work, we propose a unified framework to learn a kinematic feasibility model and a one-shot inverse mapping model for a redundant robot manipulator. Once they are trained, the models can compute the kinematic reachability of a target pose and its inverse solutions without iterative process. We validate our approach using a 7-DOF robot arm with an object grasping application.
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