108 citations · 216 across the 10 of their papers we have counts for
10 papers
From Task Allocation to Risk Clearing: A Unifying Interface for Mixed Human-Agent Societies
Vassilis Vassiliades
As humans, robots, and software agents increasingly share safety-critical environments, coordination must move from static task allocation to managing uncertain commitments. Existi…
Towards Continual Reinforcement Learning for Quadruped Robots
Giovanni Minelli, Vassilis Vassiliades
Quadruped robots have emerged as an evolving technology that currently leverages simulators to develop a robust controller capable of functioning in the real-world without the need…
Continual Learning on the Edge with TensorFlow Lite
Giorgos Demosthenous, Vassilis Vassiliades
Deploying sophisticated deep learning models on embedded devices with the purpose of solving real-world problems is a struggle using today's technology. Privacy and data limitation…
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…
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…
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…
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…
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…
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
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 so…
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).…