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Sylvain Calinon

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Learning Task Priorities from Demonstrations

Sylvain Calinon

As humanoid robots become increasingly popular, learning and control algorithms must take into account the new constraints and challenges inherent to these platforms, if we aim to fully exploit their potential. One of the most prominent of such aspects is ...

2019

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

Sylvain Calinon

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 the extreme other end of the spectrum: how can a robot adapt with only a handf ...

2019

Learning from demonstration with model-based Gaussian process

Sylvain Calinon, David Ginsbourger, Noémie Laure Gwendoline Jaquier

In learning from demonstrations, it is often desirable to adapt the behavior of the robot in function of the variability retrieved from human demonstrations and the (un)certainty encoded in different parts of the task. In this paper, we propose a novel mul ...

2019

Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

Sylvain Calinon, Noémie Laure Gwendoline Jaquier

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromi ...

2019

Learning Control

Sylvain Calinon

This chapter presents an overview of learning approaches for the acquisition of controllers and movement skills in humanoid robots. The term learning control refers to the process of acquiring a control strategy to achieve a task. While the definition is i ...

Springer2019

Bayesian Gaussian mixture model for robotic policy imitation

Sylvain Calinon, Emmanuel Pignat

A common approach to learn robotic skills is to imitate a demonstrated policy. Due to the compounding of small errors and perturbations, this approach may let the robot leave the states in which the demonstrations were provided. This requires the considera ...

2019

Improving dual-arm assembly by master-slave compliance

Sylvain Calinon, Emmanuel Pignat

In this paper we show how different choices regarding compliance affect a dual-arm assembly task. In addition, we present how the compliance parameters can be learned from a human demonstration. Compliant motions can be used in assembly tasks to mitigate p ...

2019

Learning from Demonstration (Programming by Demonstration)

Sylvain Calinon

Learning from Demonstration (LfD), also called Programming by Demonstration (PbD), refers to the process used to transfer new skills to a machine by relying on demonstrations from a user. It is inspired by the imitation capability developed by humans and a ...

Springer2019

Learning from demonstration for semi-autonomous teleoperation

Sylvain Calinon

Teleoperation in domains such as deep-sea or space often requires the completion of a set of recurrent tasks. We present a framework that uses a probabilistic approach to learn from demonstration models of manipulation tasks. We show how such a framework c ...

2019

Temporal Super-Resolution Microscopy Using a Hue-Encoded Shutter

Sylvain Calinon, Michael Stefan Daniel Liebling, Emmanuel Pignat, Christian Jaques

Limited time-resolution in microscopy is an obstacle to many biological studies. Despite recent advances in hardware, digital cameras have limited operation modes that constrain frame-rate, integration time, and color sensing patterns. In this paper, we pr ...

2019