Robot Body Schema Learning from Full-body Extero/Proprioception Sensors
<|reference_start|>Robot Body Schema Learning from Full-body Extero/Proprioception Sensors: 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.<|reference_end|>
@article{jiang2024robot,
    title={Robot Body Schema Learning from Full-body Extero/Proprioception Sensors},
    author={Shuo Jiang, Jinkun Zhang, Lawson Wong},
    journal={arXiv preprint arXiv:2402.18675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18675},
    primaryClass={cs.RO}
}