963 citations · 3.1k across the 34 of their papers we have counts for
39 papers
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…
BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions
Paul Kent, Adam Gaier, Jean-Baptiste Mouret +1
Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find many high performing points that all behave differently according t…
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…
An Online Data-Driven Emergency-Response Method for Autonomous Agents in Unforeseen Situations
Glenn Maguire, Nicholas Ketz, Praveen Pilly +1
Reinforcement learning agents perform well when presented with inputs within the distribution of those encountered during training. However, they are unable to respond effectively…
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…
Evolving the Behavior of Machines: From Micro to Macroevolution
Jean-Baptiste Mouret
Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems. This feat has inspired computer scientists since the advent of compu…
Quality-Diversity Optimization: a novel branch of stochastic optimization
Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades +1
Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highe…
Quality Diversity for Multi-task Optimization
Jean-Baptiste Mouret, Glenn Maguire
Quality Diversity (QD) algorithms are a recent family of optimization algorithms that search for a large set of diverse but high-performing solutions. In some specific situations,…
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…
Discovering Representations for Black-box Optimization
Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge -- between exploring a wide variety of solutions,…
Learning a Behavioral Repertoire from Demonstrations
Niels Justesen, Miguel Gonzalez Duque, Daniel Cabarcas Jaramillo +2
Imitation Learning (IL) is a machine learning approach to learn a policy from a dataset of demonstrations. IL can be useful to kick-start learning before applying reinforcement lea…
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…
Data-Efficient Design Exploration through Surrogate-Assisted Illumination
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. In these domains illumination algorithms, such as MAP…
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards
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…
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
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. Neuroevolution, however, has so…
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Vassilis Vassiliades, Jean-Baptiste Mouret
Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of be…
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic +50
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic pr…
Discovery of a big void in Khufu's Pyramid by observation of cosmic-ray muons
Kunihiro Morishima, Mitsuaki Kuno, Akira Nishio +31
The Great Pyramid or Khufu's Pyramid was built on the Giza Plateau (Egypt) during the IVth dynasty by the pharaoh Khufu (Cheops), who reigned from 2509 to 2483 BC. Despite being on…
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…