Publications by Emmanuel Pignat

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

2021

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Sylvain Calinon, Emmanuel Pignat, Teguh Santoso Lembono, Julius Maximilian Jankowski

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

IEEE2021

Product of experts for robot learning from demonstration

Emmanuel Pignat

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

EPFL2021

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Sylvain Calinon, Noémie Laure Gwendoline Jaquier, Emmanuel Pignat, Hakan Girgin

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this pa ...

IEEE2020

Generative adversarial training of product of policies for robust and adaptive movement primitives

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...

2020

Temporal Super-Resolution Microscopy Using a Hue-Encoded Shutter

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

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

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

Learning adaptive dressing assistance from human demonstration

Sylvain Calinon, Emmanuel Pignat

For tasks such as dressing assistance, robots should be able to adapt to different user morphologies, preferences and requirements. We propose a programming by demonstration method to efficiently learn and adapt such skills. Our method encodes sensory info ...

Elsevier2017