Communications of the ACM
Stumble-Proof Robot Adapts to Challenging Terrain in Real Time
A new model for robotic locomotion adapts in real time to any terrain it encounters, changing its gait on the fly to keep trucking when it hits sand, rocks, stairs, and other sudden changes.
Credit: Berkeley AI Research/Facebook AI Research/Carnegie Mellon University
A new robotic locomotion model capable of real-time terrain adaptation has been developed by a multi-institutional research team.
Engineers at Facebook AI, the University of California, Berkeley (UC Berkeley), and Carnegie Mellon University based Rapid Motor Adaptation (RMA) on the ability of humans and animals to quickly and unconsciously adjust their locomotion to different conditions.
The team trained the system in a virtual model of the real world, where the robot's brain learned to maximize forward motion with the least amount of energy, and to avoid falls by responding to incoming data from physical sensors.
UC Berkeley's Jitendra Malik said the robot employs absolutely no visual input, instead closely monitoring itself.
The RMA system uses a constantly running main gait-control algorithm and a parallel adaptive algorithm that watches internal readings and provides the main model adjustment data in response to terrain changes.
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