2024/02/28 by Shuo Jiang, Jiang, Shuo, Jinkun Zhang +3 · 1 citation
Engineering · Computer Science · #Modular Robots and Swarm Intelligence #Context-Aware Activity Recognition Systems #Reinforcement Learning in Robotics
paper · pdf · doi:10.48550/arxiv.2402.18675
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.