# Topics > Projects >  Creadapt's resilient hexapod robot

## Airicist

resibots.eu

youtube.com/aneoshun

Jean-Baptiste Mouret

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## Airicist

Transferability-based Behavioral Repertoire Learning in robotics 

 Published on Mar 21, 2013




> Numerous algorithms have been proposed to allow legged robots to learn to walk. However, their vast majority are devised to learn to walk along a straight line, which not sufficient to accomplish any real-world mission.
> 
> Here we introduce TBR-Learning, a new learning algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded, TBR-Learning is substantially faster than independently learning each controller. Our technique relies on two methods: (1) novelty search with local competition, which comes from the artificial life research field and (2) the transferability approach, which combines simulations and real tests to optimize a policy. We evaluate this new technique on a hexapod robot. Results show that with only a few dozens of short experiments performed on the physical robot, the algorithm learns a collection of controllers that allows the robot to reach each point of its reachable space. Overall, TBR-Learning opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

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## Airicist

Fast Damage Recovery in Robotics with The T-Resilience algorithm 

 Published on Apr 29, 2013




> Other failure scenarios are presented in: https://youtu.be/dncuBUnfkA4
> 
> Abstract:
> Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches.
> 
> Publications
> "Fast Damage Recovery in Robotics with the T-Resilience Algorithm"
> 
> by S. Koos, A. Cully and J.-B. Mouret
> 2013

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## Airicist

Creadapt's resilient hexapod robot 

Published on Feb 20, 2014

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## Airicist

Article "Hexapod Robot Gets Even Better at Being Indestructible"

by Evan Ackerman
July 28, 2014

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## Airicist

Supplementary Video S1 for "Robots that can adapt like natural animals" 

 Uploaded on Jun 29, 2014




> Title: Damage Recovery in Robots via Intelligent Trial and Error
> 
> Authors: Antoine Cully, Jeff Clune and Jean-Baptiste Mouret
> 
> The video shows the Intelligent Trial and Error Algorithm in action for two different damage conditions: a leg that has lost power and a broken leg. Initially, when the robot is undamaged, a hand-designed, classic tripod gait, performs well. Once damage occurs, however, this reference gait no longer works. The Intelligent Trial and Error Algorithm is initiated and quickly finds fast, compensatory behaviors for both damage conditions.

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## Airicist

Supplementary video S2 for "Robots that can adapt like natural animals" 

 Uploaded on Jun 29, 2014




> Title: : A Behavior Repertoire Containing Many Different Types of Walking Gaits
> Authors: Antoine Cully, Jeff Clune, Jean-Baptiste Mouret
> 
> In the behavioral repertoire creation step, the MAP-Elites algorithm produces a collection of different types of walking gaits. The video shows several examples of the different types of behaviors that are produced, from classic hexapod gaits to more unexpected forms of locomotion.

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## Airicist

Robot creadapt 

Published on Jan 21, 2015




> First steps of the Creadapt robot. This a wheel-leg hybrid robot.

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## Airicist

Robots that can adapt like animals (Nature cover article)

Published on May 27, 2015




> The Intelligent Trial and Error Algorithm introduced in the paper 'Robots that can adapt like animals' (Nature, 2015): the video shows two different robots that can adapt to a wide variety of injuries in under two minutes. 
> 
> A six-legged robot adapts to keep walking even if two of its legs are broken, and a robotic arm learns how to correctly place an object even with several broken motors.
> 
> Full citation: Cully A, Clune J, Tarapore DT, Mouret J-B. Robots that can adapt like animals. Nature, 2015. 521.7553, (cover article).

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## Airicist

Injured robots learn to limp

Published on May 27, 2015




> Like most computers, robots are highly efficient… until something goes wrong. But could they learn to adapt to mechanical faults? Scientists have been deliberately sabotaging walking robots to see how fast they learn to cope.
> 
> Read more on the story:
> "'Instinctive' robot recovers from injury fast"
> Restorative algorithm has potential to make a variety of machines resilient.
> 
> by Davide Castelvecchi
> May 27, 2015

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## Airicist

Towards semi-episodic learning for robot damage recovery

Published on Mar 31, 2016




> "Towards semi-episodic learning for robot damage recovery"
> 
> Konstantinos Chatzilygeroudis, Antoine Cully and Jean-Baptiste Mouret
> 
> Paper submitted to "Artificial Intelligence for Long-Term Autonomy" (AILTA) workshop in ICRA 2016.
> 
> Abstract:
> 
> The recently introduced Intelligent Trial and Error algorithm (IT&E) enables robots to creatively adapt to damage in a matter of minutes by combining an off-line evolutionary algorithm and an on-line learning algorithm based on Bayesian Optimization. We extend the IT&E algorithm to allow for robots to learn to compensate for damages while executing their task(s). This leads to a semi-episodic learning scheme that increases the robot’s life-time autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.
> ...

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## Airicist

Learning and adapting quadruped gaits with the "Intelligent Trial & Error'' algorithm

Published on Mar 29, 2019




> Eloïse Dalin, Pierre Desreumaux & Jean-Baptiste Mouret
> 
> We use the IT&E algorithm (Cully et al., 2015) to learn gaits for an intact quadruped robot and to adapt to damage, in less than 35 trials (usually less than 10).
> 
> This work received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (GA no. 637972, project "ResiBots")
> 
> 'Robots that can adapt like animals"
> 
> by Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret
> July 13,  2014

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