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Dive into the research topics where Sylvain Calinon is active.

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Featured researches published by Sylvain Calinon.


systems man and cybernetics | 2007

On Learning, Representing, and Generalizing a Task in a Humanoid Robot

Sylvain Calinon; Florent Guenter; Aude Billard

We present a programming-by-demonstration framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a series of experiments, in which a human demonstrator teaches a humanoid robot simple manipulatory tasks. A probability-based estimation of the relevance is suggested by first projecting the motion data onto a generic latent space using principal component analysis. The resulting signals are encoded using a mixture of Gaussian/Bernoulli distributions (Gaussian mixture model/Bernoulli mixture model). This provides a measure of the spatio-temporal correlations across the different modalities collected from the robot, which can be used to determine a metric of the imitation performance. The trajectories are then generalized using Gaussian mixture regression. Finally, we analytically compute the trajectory which optimizes the imitation metric and use this to generalize the skill to different contexts


IEEE Robotics & Automation Magazine | 2010

Learning and Reproduction of Gestures by Imitation

Sylvain Calinon; Florent D'halluin; Eric L. Sauser; Darwin G. Caldwell; Aude Billard

We presented and evaluated an approach based on HMM, GMR, and dynamical systems to allow robots to acquire new skills by imitation. Using HMM allowed us to get rid of the explicit time dependency that was considered in our previous work [12], by encapsulating precedence information within the statistical representation. In the context of separated learning and reproduction processes, this novel formulation was systematically evaluated with respect to our previous approach, LWR [20], LWPR [21], and DMPs [13]. We finally presented applications on different kinds of robots to highlight the flexibility of the proposed approach in three different learning by imitation scenarios.


Robotics and Autonomous Systems | 2004

Discovering Optimal Imitation Strategies

Aude Billard; Yann Epars; Sylvain Calinon; Stefan Schaal; Gordon Cheng

This paper develops a general policy for learning the relevant features of an imitation task. We restrict our study to imitation of manipulative tasks or gestures. The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different imitative tasks and controls task reproduction by a full body humanoid robot.


Robotics and Autonomous Systems | 2006

Discriminative and Adaptive Imitation in Uni-Manual and Bi-Manual Tasks

Aude Billard; Sylvain Calinon; Florent Guenter

This paper addresses the problems of what to imitate and how to imitate in simple uni- and bi-manual manipulatory tasks. To solve the what to imitate issue, we use a probabilistic method, based on Hidden Markov Models, for extracting the relative importance of reproducing either the gesture or the specific hand path in a given task. This allows us to determine a metric of imitation performance. To solve the how to imitate issue, we compute the trajectory that optimizes the metric, given a set of robots body constraints. We validate the methods in a series of experiments, where a human demonstrator teaches through kinesthetic a humanoid robot how to manipulate simple objects.


IEEE Transactions on Robotics | 2008

Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations

Micha Hersch; Florent Guenter; Sylvain Calinon; Aude Billard

We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment. It combines a dynamical system control approach with tools of statistical learning theory and provides a solution to the inverse kinematics problem when dealing with a redundant manipulator. The system is validated on two experiments involving a humanoid robot: putting an object into a box and reaching for and grasping an object.


intelligent robots and systems | 2010

Robot motor skill coordination with EM-based Reinforcement Learning

Petar Kormushev; Sylvain Calinon; Darwin G. Caldwell

We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the users demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.


Advanced Robotics | 2011

Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

Petar Kormushev; Sylvain Calinon; Darwin G. Caldwell

A method to learn and reproduce robot force interactions in a human–robot interaction setting is proposed. The method allows a robotic manipulator to learn to perform tasks that require exerting forces on external objects by interacting with a human operator in an unstructured environment. This is achieved by learning two aspects of a task: positional and force profiles. The positional profile is obtained from task demonstrations via kinesthetic teaching. The force profile is obtained from additional demonstrations via a haptic device. A human teacher uses the haptic device to input the desired forces that the robot should exert on external objects during the task execution. The two profiles are encoded as a mixture of dynamical systems, which is used to reproduce the task satisfying both the positional and force profiles. An active control strategy based on task-space control with variable stiffness is then proposed to reproduce the skill. The method is demonstrated with two experiments in which the robot learns an ironing task and a door-opening task.


international conference on machine learning | 2005

Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM

Sylvain Calinon; Aude Billard

This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the gesture and discards the variability intrinsic to each persons motion. We compare a decomposition into principal components (PCA) and independent components (ICA) as a first step of preprocessing in order to decorrelate and denoise the data, as well as to reduce the dimensionality of the dataset to make this one tractable. In a second stage of processing, we explore the use of a probabilistic encoding through continuous Hidden Markov Models (HMMs), as a way to encapsulate the sequential nature and intrinsic variability of the motions in stochastic finite state automata. Finally, the method is validated in a humanoid robot to reproduce a variety of gestures performed by a human demonstrator.


intelligent robots and systems | 2004

Stochastic gesture production and recognition model for a humanoid robot

Sylvain Calinon; Aude Billard

Robot programming by demonstration (PbD) aims at developing adaptive and robust controllers to enable the robot to learn new skills by observing and imitating a human demonstration. While the vast majority of PbD works has focused on systems that learn a specific subset of tasks, our work explores the problem of recognizing, generalizing, and reproducing tasks in a unified mathematical framework. The approach makes abstraction of the task and dataset at hand to tackle the general issue of learning which of the features are the relevant ones to imitate. In this paper, we present an implementation of this framework to the determination of the optimal strategy to reproduce arbitrary gestures. The model is tested and validated on a humanoid robot, using recordings of the kinematics of the demonstrators arm motion. The hand path and joint angle trajectories are encoded in hidden Markov models. The system uses the optimal prediction of the models to generate the reproduction of the motion.


international conference on robotics and automation | 2005

Goal-Directed Imitation in a Humanoid Robot

Sylvain Calinon; Florent Guenter; Aude Billard

Our work aims at developing a robust discriminant controller for robot programming by demonstration. It addresses two core issues of imitation learning, namely “what to imitate” and “how to imitate”. This paper presents a method by which a robot extracts the goals of a demonstrated task and determines the imitation strategy that satisfies best these goals. The method is validated in a humanoid platform, taking inspiration of an influential experiment from developmental psychology.

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Dive into the Sylvain Calinon's collaboration.

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Darwin G. Caldwell

Istituto Italiano di Tecnologia

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Aude Billard

École Polytechnique Fédérale de Lausanne

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Leonel Dario Rozo

Istituto Italiano di Tecnologia

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Danilo Bruno

Istituto Italiano di Tecnologia

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Petar Kormushev

Istituto Italiano di Tecnologia

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Florent Guenter

École Normale Supérieure

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João Silvério

Istituto Italiano di Tecnologia

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Martijn J.A. Zeestraten

Istituto Italiano di Tecnologia

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