no code implementations • 10 Oct 2024 • Thomas Martin, Adrien Guénard, Vladislav Tempez, Lucien Renaud, Thibaut Raharijaona, Franck Ruffier, Jean-Baptiste Mouret
However, hovering inside air ducts is problematic due to the airflow generated by the rotors, which recirculates inside the duct and destabilizes the drone, whereas hovering is a key feature for many inspection missions.
1 code implementation • 19 Jul 2024 • Dionis Totsila, Quentin Rouxel, Jean-Baptiste Mouret, Serena Ivaldi
This paper presents Words2Contact, a language-guided multi-contact placement pipeline leveraging large language models and vision language models.
no code implementations • 2 Feb 2024 • Timothée Anne, Jean-Baptiste Mouret
In this paper, we introduce Parametric-Task MAP-Elites (PT-ME), a new black-box algorithm for continuous multi-task optimization problems.
1 code implementation • 1 Mar 2022 • Timothée Anne, Eloïse Dalin, Ivan Bergonzani, Serena Ivaldi, Jean-Baptiste Mouret
This article introduces a method, called D-Reflex, that learns a neural network that chooses this contact position given the wall orientation, the wall distance, and the posture of the robot.
no code implementations • 1 Mar 2022 • Yoann Fleytoux, Anji Ma, Serena Ivaldi, Jean-Baptiste Mouret
Our pipeline is based on learning a latent space of grasps with a dataset generated with any state-of-the-art grasp generator (e. g., Dex-Net).
no code implementations • 17 Dec 2021 • Glenn Maguire, Nicholas Ketz, Praveen Pilly, Jean-Baptiste Mouret
We demonstrate the potential of this approach in a simulated 3D car driving scenario, in which the agent devises a response in under 2 seconds to avoid collisions with objects it has not seen during training.
no code implementations • 2 Jul 2021 • Luigi Penco, Jean-Baptiste Mouret, Serena Ivaldi
Humanoid robots could be versatile and intuitive human avatars that operate remotely in inaccessible places: the robot could reproduce in the remote location the movements of an operator equipped with a wearable motion capture device while sending visual feedback to the operator.
no code implementations • 21 Dec 2020 • Jean-Baptiste Mouret
Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems.
2 code implementations • 8 Dec 2020 • Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades, Jean-Baptiste Mouret
In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community.
1 code implementation • 10 Mar 2020 • Rituraj Kaushik, Timothée Anne, Jean-Baptiste Mouret
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dynamics of the system with only a few data-points.
2 code implementations • 9 Mar 2020 • Jean-Baptiste Mouret, Glenn Maguire
However, they cannot solve multiple tasks when the fitness needs to be evaluated independently for each task (e. g., optimizing policies to grasp many different objects).
1 code implementation • 9 Mar 2020 • Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions -- but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators.
1 code implementation • 16 Jul 2019 • Rituraj Kaushik, Pierre Desreumaux, Jean-Baptiste Mouret
Repertoire-based learning is a data-efficient adaptation approach based on a two-step process in which (1) a large and diverse set of policies is learned in simulation, and (2) a planning or learning algorithm chooses the most appropriate policies according to the current situation (e. g., a damaged robot, a new object, etc.).
no code implementations • 5 Jul 2019 • Niels Justesen, Miguel Gonzalez Duque, Daniel Cabarcas Jaramillo, Jean-Baptiste Mouret, Sebastian Risi
Imitation Learning (IL) is a machine learning approach to learn a policy from a dataset of demonstrations.
no code implementations • 17 Jan 2019 • David Howard, Agoston E. Eiben, Danielle Frances Kennedy, Jean-Baptiste Mouret, Philip Valencia, Dave Winkler
Natural lifeforms specialise to their environmental niches across many levels; from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs, and overarching body plans.
no code implementations • 6 Jul 2018 • Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Freek Stulp, Sylvain Calinon, Jean-Baptiste Mouret
Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot.
1 code implementation • 25 Jun 2018 • Rituraj Kaushik, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties.
2 code implementations • 15 Jun 2018 • Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs.
1 code implementation • 15 Apr 2018 • Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms.
1 code implementation • 11 Apr 2018 • Vassilis Vassiliades, Jean-Baptiste Mouret
Evolution has produced an astonishing diversity of species, each filling a different niche.
no code implementations • 9 Mar 2018 • Joel Lehman, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley, Samuel Bernard, Guillaume Beslon, David M. Bryson, Patryk Chrabaszcz, Nick Cheney, Antoine Cully, Stephane Doncieux, Fred C. Dyer, Kai Olav Ellefsen, Robert Feldt, Stephan Fischer, Stephanie Forrest, Antoine Frénoy, Christian Gagné, Leni Le Goff, Laura M. Grabowski, Babak Hodjat, Frank Hutter, Laurent Keller, Carole Knibbe, Peter Krcah, Richard E. Lenski, Hod Lipson, Robert MacCurdy, Carlos Maestre, Risto Miikkulainen, Sara Mitri, David E. Moriarty, Jean-Baptiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Parsons, Robert T. Pennock, William F. Punch, Thomas S. Ray, Marc Schoenauer, Eric Shulte, Karl Sims, Kenneth O. Stanley, François Taddei, Danesh Tarapore, Simon Thibault, Westley Weimer, Richard Watson, Jason Yosinski
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
2 code implementations • 20 Sep 2017 • Rémi Pautrat, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e. g., from simulation or from previous tasks) to accelerate learning on a robot.
1 code implementation • 20 Sep 2017 • Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties.
1 code implementation • 21 Mar 2017 • Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik, Dorian Goepp, Vassilis Vassiliades, Jean-Baptiste Mouret
The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties.
4 code implementations • 13 Feb 2017 • Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user.
no code implementations • 10 Feb 2017 • John Rieffel, Jean-Baptiste Mouret
Living organisms intertwine soft (e. g., muscle) and hard (e. g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots.
no code implementations • 28 Nov 2016 • Vaios Papaspyros, Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Jean-Baptiste Mouret
We compare our new "safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in which the safety constraints are dealt as separate objectives.
1 code implementation • 22 Nov 2016 • Antoine Cully, Konstantinos Chatzilygeroudis, Federico Allocati, Jean-Baptiste Mouret
Limbo is an open-source C++11 library for Bayesian optimization which is designed to be both highly flexible and very fast.
5 code implementations • 18 Oct 2016 • Vassilis Vassiliades, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
The recently introduced Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run.
1 code implementation • 13 Oct 2016 • Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Jean-Baptiste Mouret
However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously.
no code implementations • 5 Oct 2016 • Konstantinos Chatzilygeroudis, Antoine Cully, Jean-Baptiste Mouret
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.
no code implementations • 4 Oct 2016 • Jean-Baptiste Mouret
Many fields are now snowed under with an avalanche of data, which raises considerable challenges for computer scientists.
no code implementations • 24 May 2016 • Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek, Jean-Baptiste Mouret, Michael A. Osborne, Shimon Whiteson
ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but play a substantial role in determining the optimal policy.
no code implementations • 23 May 2015 • Henok Mengistu, Joost Huizinga, Jean-Baptiste Mouret, Jeff Clune
Hierarchical organization -- the recursive composition of sub-modules -- is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet.
7 code implementations • 20 Apr 2015 • Jean-Baptiste Mouret, Jeff Clune
Interestingly, because MAP-Elites explores more of the search space, it also tends to find a better overall solution than state-of-the-art search algorithms.
no code implementations • 18 Oct 2014 • Danesh Tarapore, Jean-Baptiste Mouret
and fitness values (how different is the fitness after a mutation?).
2 code implementations • 13 Jul 2014 • Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret
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.
no code implementations • 11 Jul 2012 • Jeff Clune, Jean-Baptiste Mouret, Hod Lipson
A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments).