Sylvain Koos
ABSTRACT
No abstract available.
- J. Bongard, V. Zykov, and H. Lipson. Resilient machines through continuous self-modeling. Science, 314(5802):1118--1121, 2006.Google Scholar
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002. Google ScholarDigital Library
- F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers, and W. Burgard. An evaluation of the RGB-D SLAM system. In Proc. of ICRA, 2012.Google ScholarCross Ref
- H. H. Hoos and T. Stützle. Stochastic local search: Foundations and applications. Morgan Kaufmann, 2005. Google ScholarDigital Library
- G. Hornby, S. Takamura, T. Yamamoto, and M. Fujita. Autonomous evolution of dynamic gaits with two quadruped robots. IEEE Transactions on Robotics, 21(3):402--410, 2005. Google ScholarDigital Library
- N. Jakobi, P. Husbands, and I. Harvey. Noise and the reality gap: The use of simulation in evolutionary robotics. Proc. of ECAL, pages 704--720, 1995. Google ScholarDigital Library
- N. Kohl and P. Stone. Policy gradient reinforcement learning for fast quadrupedal locomotion. In Proc. of ICRA, volume 3, pages 2619--2624. IEEE, 2004.Google ScholarCross Ref
- S. Koos, A. Cully, and J.-B. Mouret. Fast damage recovery in robotics with the t-resilience algorithm. International Journal of Robotics Research, 2013.Google ScholarDigital Library
- S. Koos, J.-B. Mouret, and S. Doncieux. The transferability approach: Crossing the reality gap in evolutionary robotics. IEEE Transactions on Evolutionary Computation, 17:122--145, 2013.Google ScholarDigital Library
- J.-B. Mouret and S. Doncieux. Sferes_v2: Evolvin' in the Multi-Core World. In Proc. of IEEE CEC, pages 4079--4086, 2010.Google Scholar
- J. Togelius, T. Schaul, D. Wierstra, C. Igel, and J. Schmidhuber. Ontogenetic and phylogenetic reinforcement learning. Kuenstliche Intelligenz, pages 30--33, 2009.Google Scholar
- J. Yosinski, J. Clune, D. Hidalgo, S. Nguyen, J. Zagal, and H. Lipson. Evolving Robot Gaits in Hardware: the HyperNEAT Generative Encoding Vs. Parameter Optimization. Proc. of ECAL, pages 1--8, 2011.Google Scholar
Index Terms
- High resilience in robotics with a multi-objective evolutionary algorithm
Recommendations
Autonomous evolution of dynamic gaits with two quadruped robots
A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our ...
Fast damage recovery in robotics with the T-resilience algorithm
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 potential damage in order to have a contingency plan ready. As an ...
Some distance measures for morphological diversification in generative evolutionary robotics
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary ComputationEvolutionary robotics often involves optimization in large, complex search spaces, requiring good population diversity. Recently, measures to actively increase diversity or novelty have been employed in order to get sufficient exploration of the search ...
Comments