Robots learn resilience by observing their motions

Robots could become more resilient and need less human intervention by self-modelling with a camera and deep neural networks.

A robot observes its reflection in a mirror, learning its own morphology and kinematics for autonomous self-simulation. The process highlights the intersection of vision-based learning and robotics, where the robot refines its movements and predicts its spatial motion through self-observation
A robot observes its reflection in a mirror, learning its own morphology and kinematics for autonomous self-simulation. The process highlights the intersection of vision-based learning and robotics, where the robot refines its movements and predicts its spatial motion through self-observation - Jane Nisselson/Columbia Engineering

To this end, researchers at Columbia University have tasked robots with teaching themselves about the structure of their own bodies and how they move by watching their motions with a camera.

With this knowledge, the robots could plan their own actions and overcome damage to their bodies.

"Like humans learning to dance by watching their mirror reflection, robots now use raw video to build kinematic self-awareness," said study lead author Yuhang Hu, a doctoral student at the Creative Machines Lab at Columbia University. "Our goal is a robot that understands its own body, adapts to damage, and learns new skills without constant human programming."

Most robots first learn to move in simulations. Once a robot can move in these virtual environments, it enters the physical world where it continues to learn. “The better and more realistic the simulator, the easier it is for the robot to make the leap from simulation into reality,” said Hod Lipson, James and Sally Scapa Professor of Innovation and chair of the Department of Mechanical Engineering.

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