> Machine learning for resilient & creative robots

We develop novel learning algorithms for creative adaptation in robotics.

Highlight: one of our papers is on the cover of Nature! See our press material page for videos, FAQ, etc.

The paper is available online on Nature's website. A pre-review, author generated draft is available here: [pdf]

Main projects

  • ERC Starting grant ResiBots (grant agreement No 637972).
  • ANR project Creadapt, ANR-12-JS03-0009)

Despite over 50 years of research in robotics, most existing robots are far from being as resilient as the simplest animals: they are fragile machines that easily stop functioning in difficult conditions. The goal of this project is to radically change this situation by providing the algorithmic foundations for low-cost robots that can autonomously recover from unforeseen damages in a few minutes.

The current approach to fault tolerance is inherited from safety-critical systems (e.g. spaceships or nuclear plants). It is inappropriate for low-cost autonomous robots because it relies on diagnosis procedures, which require expensive proprioceptive sensors, and contingency plans, which cannot cover all the possible situations that an autonomous robot can encounter.

It is here contended that trial-and-error learning algorithms provide an alternative approach that does not require diagnosis, nor pre-defined contingency plans. In this project, we will develop and study a novel family of such learning algorithms that make it possible for autonomous robots to quickly discover compensatory behaviors. We will thus shed a new light on one of the most fundamental questions of robotics: how can a robot be as adaptive as an animal? The techniques developed in this project will substantially increase the lifespan of robots without increasing their cost and open new research avenues for adaptive machines.

Main publications

Pre-prints / arxiv


J. Rieffel*, J.-B Mouret* (2017). Soft Tensegrity Robots.
arXiv preprint. (* J. Rieffel and J.-B. Mouret contributed equally to this work).[url]

S. Paul, K. Chatzilygeroudis, K. Ciosek, J.-B Mouret, M. Osborne, S. Whiteson (2017). Alternating Optimisation and Quadrature for Robust Reinforcement Learning.
arXiv preprint. [url]


A. Cully, K. Chatzilygeroudis, F. Allocati, J.-B Mouret (2016). Limbo: A fast and flexible library for bayesian optimization.
arXiv preprint. [url]

K. Chatzilygeroudis, V. Vassiliades, J.-B Mouret (2016). Reset-free Trial-and-Error Learning for Data-Efficient Robot Damage Recovery.
arXiv preprint. [url]


J.-B Mouret, J. Clune (2015). Illuminating search spaces by mapping elites.
arXiv preprint. [url]

Articles in peer-reviewed journals


A. Cully, J.-B Mouret (2016). Evolving a Behavioral Repertoire for a Walking Robot.
Evolutionary Computation. 24. (1) 59-88. MIT Press. 10.1145/2463372.2463399
→ [pdf] [url] [video]


A. Cully, J. Clune, D. Tarapore, J.-B Mouret (2015). Robots that can adapt like animals.
Nature. 521. (7553) 503-507. Nature Publishing Group. 10.1038/nature14422
→ [pdf] [url] [source code] [video] [video]


S. Koos, A. Cully, J.-B Mouret (2013). Fast Damage Recovery in Robotics with the T-Resilience Algorithm.
International Journal of Robotics Research (IJRR). 32. (14) 1700-1723. SAGE Publications. 10.1177/0278364913499192
→ [pdf] [url]

Articles in peer-reviewed conferences


K. Chatzilygeroudis, R. Rama, R. Kaushik, D. Goepp, V. Vassiliades, J.-B Mouret (2017). Black-Box Data-efficient Policy Search for Robotics.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
→ [pdf] [url] [source code] [video]

J. Spitz, K. Bouyarmane, S. Ivaldi, J.-B Mouret (2017). Trial-and-Error Learning of Repulsors for Humanoid QP-based Whole-Body Control.
Proc. of IEEE Humanoids
→ [pdf] [video]


D. Tarapore, J. Clune, A. Cully, J.-B Mouret (2016). How Do Different Encodings Influence the Performance of the MAP-Elites Algorithm?.
Proc. of GECCO ACM.
→ [pdf] [url] [source code]


J.-M Jehanno, A. Cully, C. Grand, J.-B Mouret (2014). Design of a Wheel-Legged Hexapod Robot for Creative Adaptation.
17th International Conference on Climbing and Walking Robots (CLAWAR) 267-276. 10.1142/9789814623353_0032
→ [pdf] [video]


M. Oliveira, S. Doncieux, J.-B Mouret, C. Peixoto Santos (2013). Optimization of Humanoid Walking Controller: Crossing the Reality Gap.
Proc. of IEEE Humanoids 1-7. 10.1109/HUMANOIDS.2013.7029963
→ [pdf]


S. Koos, J.-B Mouret (2011). Online Discovery of Locomotion Modes for Wheel-Legged Hybrid Robots: a Transferability-based Approach.
14th International Conference on Climbing and Walking Robots (CLAWAR). Highly Recommended Paper, category ``control of CLAWAR'' 70-77. 10.1142/9789814374286_0008
→ [pdf] [url] [video] [video] [video]