49 citations · 93 across the 8 of their papers we have counts for
10 papers
Motion Mappings for Continuous Bilateral Teleoperation
Xiao Gao, João Silvério, Emmanuel Pignat +3
Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smo…
Generative adversarial training of product of policies for robust and adaptive movement primitives
Emmanuel Pignat, Hakan Girgin, Sylvain Calinon
In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and spee…
Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network
Teguh Santoso Lembono, Emmanuel Pignat, Julius Jankowski +1
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…
Learning from demonstration using products of experts: applications to manipulation and task prioritization
Emmanuel Pignat, João Silvério, Sylvain Calinon
Probability distributions are key components of many learning from demonstration (LfD) approaches. While the configuration of a manipulator is defined by its joint angles, poses ar…
Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
Hakan Girgin, Emmanuel Pignat, Noémie Jaquier +1
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 grea…
Interaction-limited Inverse Reinforcement Learning
Martin Troussard, Emmanuel Pignat, Parameswaran Kamalaruban +2
This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training. Our…