3 citations · 3 across the 2 of their papers we have counts for
6 papers
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…
Memory of Motion for Warm-starting Trajectory Optimization
Teguh Santoso Lembono, Antonio Paolillo, Emmanuel Pignat +1
Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of r…
Variational Inference with Mixture Model Approximation: Robotic Applications
Emmanuel Pignat, Teguh Lembono, Sylvain Calinon
We propose a method to approximate the distribution of robot configurations satisfying multiple objectives. Our approach uses variational inference, a popular method in Bayesian co…
Bayesian Gaussian mixture model for robotic policy imitation
Emmanuel Pignat, Sylvain Calinon
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 s…
Improving dual-arm assembly by master-slave compliance
Markku Suomalainen, Sylvain Calinon, Emmanuel Pignat +1
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…