TL;DR
This paper presents an innovative algorithm enabling robots to rapidly adapt to damage through intelligent trial-and-error, eliminating the need for pre-programmed contingency plans and mimicking animal-like resilience.
Contribution
The authors introduce a novel adaptive algorithm that allows robots to quickly find compensatory behaviors after damage without prior diagnosis or predefined plans.
Findings
Robots adapted to five different leg injuries in under two minutes.
Robotic arm successfully compensated for 14 different joint damages.
Algorithm outperforms traditional pre-programmed contingency approaches.
Abstract
As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing…
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