Hexapod Figures Out How to Walk After You Chop Its Leg Off

It's going to take more than a missing leg to slow this robot down

2 min read

Evan Ackerman is IEEE Spectrum’s robotics editor.

Hexapod Figures Out How to Walk After You Chop Its Leg Off

If the movies have taught us anything, it's that chopping a futuristic death robot's leg off does not significantly diminish its capacity to hunt you down. Want to know where that capacity for being utterly unstoppable came from? It's this, right here.

After just 20 minutes worth of iterations, in the above video example the robot has come up with a five-legged gait that moves it along at 18 cm/s, as compared to the undamaged 26 cm/s gait. Not bad, considering that an unmodified five-legged gait had it limping along at just 8 cm/s.

The cool bit about this recovery model is that it doesn't require any specific information about what parts are malfunctioning or missing. Instead, it's just got a known model of how it's supposed to work, and if the actual performance that it measures is less efficient, it starts searching for new behaviors. To get all sciencey about it: "the robot will thus be able to sustain a functioning behavior when damage occurs by learning to avoid behaviors that it is unable to achieve in the real world."

Things get even more interesting when the authors of this paper (Sylvain Koos, Antoine Cully and Jean-Baptiste Mouret with the ISIR, Universite Pierre et Marie Curie-Paris) start to think about what this robot is doing in terms of what injured humans do:

Overall, learning to predict what behaviors should be avoided is a very general concept that can be applied to many situations in which a robot has to autonomously adapt its behavior. On a higher level, this concept could also share some similarities with what human do when they are injured: if a movement is painful, humans do not fully understand what cause the pain, but they identify the behaviors that cause the pain; once they know that some move are painful, they learn to instinctively avoid them. Humans seem reluctant to permanently change their self-model to reflect what behaviors are possible: people with an immobilized leg still know how to activate their muscles, amputated people frequently report pain in their missing members (Ramachandran and Hirstein, 1998) and dream about themselves in their intact body (Mulder et al., 2008). Humans may therefore learn by combining their self-model with a second model that predicts which behaviors should be avoided, even if they are possible. This model would be similar in essence to a transferability function.

You can read the entire paper (hooray!) at the link below.

[ Fast Damage Recovery in Robotics with the T-Resilience Algorithm ] via [ ISIR ]

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