Teaching Robots to Build Simulations of Themselves
Yuhang Hu Jiong Lin Hod Lipson
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.