
Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series
This chapter presents an overview of techniques used for the analysis, e...
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Ergodic Exploration using Tensor Train: Applications in Insertion Tasks
By generating control policies that create natural search behaviors in a...
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Probabilistic Iterative LQR for Short Time Horizon MPC
Optimal control is often used in robotics for planning a trajectory to a...
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Motion Mappings for Continuous Bilateral Teleoperation
Mapping operator motions to a robot is a key problem in teleoperation. D...
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Generative Adversarial Network to Learn Valid Distributions of Robot Configurations for Inverse Kinematics and Constrained Motion Planning
In high dimensional robotic system, the manifold of the valid configurat...
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Generative adversarial training of product of policies for robust and adaptive movement primitives
In learning from demonstrations, many generative models of trajectories ...
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A Laserbased Dualarm System for Precise Control of Collaborative Robots
Collaborative robots offer increased interaction capabilities at relativ...
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Learning from demonstration using products of experts: applications to manipulation and task prioritization
Probability distributions are key components of many learning from demon...
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Active Improvement of Control Policies with Bayesian Gaussian Mixture Model
Learning from demonstration (LfD) is an intuitive framework allowing non...
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Analysis and Transfer of Human Movement Manipulability in Industrylike Activities
Humans exhibit outstanding learning, planning and adaptation capabilitie...
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Interactionlimited Inverse Reinforcement Learning
This paper proposes an inverse reinforcement learning (IRL) framework to...
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Learning,Generating and Adapting Wave Gestures for Expressive HumanRobot Interaction
This study proposes a novel imitation learning approach for the stochast...
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A memory of motion for visual predictive control tasks
This paper addresses the problem of efficiently achieving visual predict...
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Using a memory of motion to efficiently achieve visual predictive control tasks
This paper addresses the problem of efficiently achieving visual predict...
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Learning How to Walk: Warmstarting Optimal Control Solver with Memory of Motion
In this paper, we propose a framework to build a memory of motion to war...
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Learning from demonstration with modelbased Gaussian process
In learning from demonstrations, it is often desirable to adapt the beha...
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Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Bayesian optimization (BO) recently became popular in robotics to optimi...
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Gaussians on Riemannian Manifolds for Robot Learning and Adaptive Control
This paper presents an overview of robot learning and adaptive control a...
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Memory of Motion for Warmstarting Trajectory Optimization
Trajectory optimization for motion planning requires a good initial gues...
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Nullspace Structure in Model Predictive Control
Robotic tasks can be accomplished by exploiting different forms of redun...
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Variational Inference with Mixture Model Approximation: Robotic Applications
We propose a method to approximate the distribution of robot configurati...
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Bayesian Gaussian mixture model for robotic policy imitation
A common approach to learn robotic skills is to imitate a policy demonst...
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Tensorvariate Mixture of Experts
When data are organized in matrices or arrays of higher dimensions (tens...
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Improving dualarm assembly by masterslave compliance
In this paper we show how different choices regarding compliance affect ...
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Geometryaware Manipulability Transfer
Body posture influences human and robots performance in manipulation tas...
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Generalizing Robot Imitation Learning with Invariant Hidden SemiMarkov Models
Generalizing manipulation skills to new situations requires extracting i...
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A survey on policy search algorithms for learning robot controllers in a handful of trials
Most policy search algorithms require thousands of training episodes to ...
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A Learning from Demonstration Approach fusing Torque Controllers
Torque controllers have become commonplace in the new generation of robo...
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Sylvain Calinon
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