Teaching Robots to Build Simulations of Themselves

• URL: http://arxiv.org/abs/2311.12151v1
• Date: Mon, 20 Nov 2023 20:03:34 GMT
• Title: Teaching Robots to Build Simulations of Themselves
• Authors: Yuhang Hu, Jiong Lin, Hod Lipson
• Abstract summary: We introduce a self-supervised learning framework to enable robots model and predict their morphology, kinematics and motor control using only brief raw video data. By observing their own movements, robots learn an ability to simulate themselves and predict their spatial motion for various tasks.
• Score: 7.886658271375681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulation enables robots to plan and estimate the outcomes of prospective actions without the need to physically execute them. We introduce a self-supervised learning framework to enable robots model and predict their morphology, kinematics and motor control using only brief raw video data, eliminating the need for extensive real-world data collection and kinematic priors. By observing their own movements, akin to humans watching their reflection in a mirror, robots learn an ability to simulate themselves and predict their spatial motion for various tasks. Our results demonstrate that this self-learned simulation not only enables accurate motion planning but also allows the robot to detect abnormalities and recover from damage.

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