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arXiv · 2402.18675

Robot Body Schema Learning from Full-body Extero/Proprioception Sensors

Shuo Jiang·Jinkun Zhang·Lawson Wong
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Abstract

For a robot, its body structure is an a-prior knowledge when it is designed. However, when such information is not available, can a robot recognize it by itself? In this paper, we aim to grant a robot such ability to learn its body structure from exteroception and proprioception data collected from on-body sensors. By a novel machine learning method, the robot can learn a binary Heterogeneous Dependency Matrix from its sensor readings. We showed such matrix is equivalent to a Heterogeneous out-tree structure which can uniquely represent the robot body topology. We explored the properties of such matrix and the out-tree, and proposed a remedy to fix them when they are contaminated by partial observability or data noise. We ran our algorithm on 6 different robots with different body structures in simulation and 1 real robot. Our algorithm correctly recognized their body structures with only on-body sensor readings but no topology prior knowledge.

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Shuo Jiang, Jinkun Zhang, Lawson Wong. 2024-02-28. Robot Body Schema Learning from Full-body Extero/Proprioception Sensors. https://arxiv.org/abs/2402.18675

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