963 citations · 3.1k across the 34 of their papers we have counts for
19 papers · 1 filter
Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction
Quentin Rouxel, Ruoshi Wen, Zhibin Li +3
This short paper outlines two recent works on multi-contact teleoperation and the development of the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) framework. SEIKO…
Data-efficient learning of object-centric grasp preferences
Yoann Fleytoux, Anji Ma, Serena Ivaldi +1
Grasping made impressive progress during the last few years thanks to deep learning. However, there are many objects for which it is not possible to choose a grasp by only looking…
First do not fall: learning to exploit a wall with a damaged humanoid robot
Timothée Anne, Eloïse Dalin, Ivan Bergonzani +2
Humanoid robots could replace humans in hazardous situations but most of such situations are equally dangerous for them, which means that they have a high chance of being damaged a…
VP-GO: a "light" action-conditioned visual prediction model
Anji Ma, Yoann Fleytoux, Jean-Bapstiste Mouret +1
Visual prediction models are a promising solution for visual-based robotic grasping of cluttered, unknown soft objects. Previous models from the literature are computationally gree…
Signal-based self-organization of a chain of UAVs for subterranean exploration
Pierre Laclau, Vladislav Tempez, Franck Ruffier +2
Miniature multi-rotors are promising robots for navigating subterranean networks, but maintaining a radio connection underground is challenging. In this paper, we introduce a distr…
Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
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…
Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics
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 p…
Evolving embodied intelligence from materials to machines
David Howard, Agoston E. Eiben, Danielle Frances Kennedy +3
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, l…
A survey on policy search algorithms for learning robot controllers in a handful of trials
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Freek Stulp +2
Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on th…
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search
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 l…
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics
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…
Black-Box Data-efficient Policy Search for Robotics
Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik +3
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 th…
Adaptive and Resilient Soft Tensegrity Robots
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. Th…
Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors
Vaios Papaspyros, Konstantinos Chatzilygeroudis, Vassilis Vassiliades +1
The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two…
Towards semi-episodic learning for robot damage recovery
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 algo…
Reset-free Trial-and-Error Learning for Robot Damage Recovery
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Jean-Baptiste Mouret
The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue).…
Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore +1
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…
Evolving a Behavioral Repertoire for a Walking Robot
Antoine Cully, Jean-Baptiste Mouret
Numerous algorithms have been proposed to allow legged robots to learn to walk. However, the vast majority of these algorithms is devised to learn to walk in a straight line, which…
Crossing the Reality Gap: a Short Introduction to the Transferability Approach
Jean-Baptiste Mouret, Sylvain Koos, Stéphane Doncieux
In robotics, gradient-free optimization algorithms (e.g. evolutionary algorithms) are often used only in simulation because they require the evaluation of many candidate solutions.…