Search Results for author: Konstantinos Chatzilygeroudis

Found 17 papers, 11 papers with code

End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies

no code implementations22 May 2023 Dionis Totsila, Konstantinos Chatzilygeroudis, Denis Hadjivelichkov, Valerio Modugno, Ioannis Hatzilygeroudis, Dimitrios Kanoulas

In this work, we bridge the gap between generic neural network policies and dynamical system-based policies, and we introduce Autonomous Neural Dynamic Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b) always produce asymptotically stable behaviors, and (c) are more flexible than traditional stable dynamical system-based policies.

Imitation Learning

Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity

no code implementations18 Oct 2022 Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis, Bryan Lim, Antoine Cully

Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned skills.

Hierarchical Quality-Diversity for Online Damage Recovery

1 code implementation12 Apr 2022 Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis, Antoine Cully

These adaptation capabilities are directly linked to the behavioural diversity in the repertoire.

Learning of Parameters in Behavior Trees for Movement Skills

1 code implementation27 Sep 2021 Matthias Mayr, Konstantinos Chatzilygeroudis, Faseeh Ahmad, Luigi Nardi, Volker Krueger

Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error.

Reinforcement Learning (RL)

Quality-Diversity Optimization: a novel branch of stochastic optimization

1 code implementation8 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.

Stochastic Optimization

A survey on policy search algorithms for learning robot controllers in a handful of trials

no code implementations6 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.

Bayesian Optimization

Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards

1 code implementation25 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.

Continuous Control Efficient Exploration

Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics

1 code implementation20 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.

Continuous Control

Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search

1 code implementation20 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.

Bayesian Optimization Transfer Learning

Black-Box Data-efficient Policy Search for Robotics

1 code implementation21 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.

Continuous Control Reinforcement Learning (RL)

Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors

no code implementations28 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.

Bayesian Optimization

Limbo: A Fast and Flexible Library for Bayesian Optimization

1 code implementation22 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.

Bayesian Optimization

Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm

5 code implementations18 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.

Reset-free Trial-and-Error Learning for Robot Damage Recovery

1 code implementation13 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.

Reinforcement Learning (RL) RTE

Towards semi-episodic learning for robot damage recovery

no code implementations5 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.

Bayesian Optimization

Alternating Optimisation and Quadrature for Robust Control

no code implementations24 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.

Bayesian Optimisation

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