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High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning
April 22, 2024, 4:43 a.m. | Lennart Schulze, Hod Lipson
cs.LG updates on arXiv.org arxiv.org
Abstract: A robot self-model is a task-agnostic representation of the robot's physical morphology that can be used for motion planning tasks in the absence of a classical geometric kinematic model. In particular, when the latter is hard to engineer or the robot's kinematics change unexpectedly, human-free self-modeling is a necessary feature of truly autonomous agents. In this work, we leverage neural fields to allow a robot to self-model its kinematics as a neural-implicit query model learned …
abstract arxiv change cs.cv cs.lg cs.ro dynamic engineer fields free freedom human modeling motion planning planning representation robot tasks type
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