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Robots that can adapt like animals

Robots that can adapt like animals
Robots that can adapt like animals
Robots have transformed many industries, most notably manufacturing1, and have the power to deliver tremendous benefits to society, such as in search and rescue2, disaster response3, health care4 and transportation5. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets6 to deep oceans7. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility6,8. Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes9, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots6,8. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage10,11, but current techniques are slow even with small, constrained search spaces12. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.
0028-0836
503-507
Cully, Antoine
ae321e02-82f4-44cb-bfa0-31a265ebaaf1
Clune, Jeff
2a9284aa-86d9-4279-aefe-c938cf31ad4d
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Mouret, Jean-Baptiste
a837dbc0-1852-4e6f-93d8-41d927305eaf
Cully, Antoine
ae321e02-82f4-44cb-bfa0-31a265ebaaf1
Clune, Jeff
2a9284aa-86d9-4279-aefe-c938cf31ad4d
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Mouret, Jean-Baptiste
a837dbc0-1852-4e6f-93d8-41d927305eaf

Cully, Antoine, Clune, Jeff, Tarapore, Danesh and Mouret, Jean-Baptiste (2015) Robots that can adapt like animals. Nature, 521 (7553), 503-507. (doi:10.1038/nature14422).

Record type: Letter

Abstract

Robots have transformed many industries, most notably manufacturing1, and have the power to deliver tremendous benefits to society, such as in search and rescue2, disaster response3, health care4 and transportation5. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets6 to deep oceans7. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility6,8. Whereas animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes9, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots6,8. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage10,11, but current techniques are slow even with small, constrained search spaces12. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.

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2015ACLI3468 - Author's Original
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More information

Accepted/In Press date: 17 March 2015
e-pub ahead of print date: 27 May 2015
Published date: 27 May 2015
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 410785
URI: http://eprints.soton.ac.uk/id/eprint/410785
ISSN: 0028-0836
PURE UUID: df283f8e-6869-4407-8f64-2d783c60f6ad
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 09 Jun 2017 09:38
Last modified: 18 Feb 2021 17:31

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