Publications de Eric Sauser | EPFL Graph Search

Iterative Learning of Grasp Adaptation through Human Corrections

Aude Billard, Eric Sauser, Brenna Dee Argall

In the context of object interaction and manipulation, one characteristic of a robust grasp is its ability to comply with external perturbations applied to the grasped object while still maintaining the grasp. In this work we introduce an approach for gras ...

Elsevier2012

Evaluation of a probabilistic approach to learn and reproduce gestures by imitation

Aude Billard, Eric Sauser, Sylvain Calinon

We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learn robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time- ...

2010

Tactile Guidance for Policy Refinement and Reuse

Aude Billard, Eric Sauser, Brenna Dee Argall

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations can degrade policy performance. This work presents an approach for policy improvement a ...

2010

Policy Adaptation through Tactile Correction

Aude Billard, Eric Sauser, Brenna Dee Argall

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations, for example correspondence issues between the robot and demonstrator, can degrade poli ...

2010

Learning and reproduction of gestures by imitation: An approach based on Hidden Markov Model and Gaussian Mixture Regression

Aude Billard, Eric Sauser, Sylvain Calinon, Florent D'Halluin

We present a probabilistic approach to learn robust models of human motion through imitation. The association of Hidden Markov Model (HMM), Gaussian Mixture Regression (GMR) and dynamical systems allows us to extract redundancies across multiple demonstrat ...

Institute of Electrical and Electronics Engineers2010

Tactile Guidance for Policy Adaptation

Aude Billard, Eric Sauser, Brenna Dee Argall

Demonstration learning is a powerful and practical technique to develop robot behaviors. Even so, development remains a challenge and possible demonstration limitations, for example correspondence issues between the robot and demonstrator, can degrade poli ...

2010

Tactile Correction and Multiple Training Data Sources for Robot Motion Control

Aude Billard, Eric Sauser, Brenna Dee Argall

This work considers our approach to robot motion control learning from the standpoint of multiple data sources. Our paradigm derives data from human teachers providing task demonstrations and tactile corrections for policy refinement and reuse. We contribu ...

2009

Online learning of the body schema

Micha Hersch, Aude Billard, Eric Sauser

We present an algorithm enabling a humanoid robot to visually learn its body schema, knowing only the number of degrees of freedom in each limb. By “body schema” we mean the joint positions and orientations and thus the kinematic function. The learning is ...

2008

Neural modeling of the cortical processes underlying imitative behaviors : a dynamic neural field approach

Eric Sauser

Imitation is the ability to recognize, learn and reproduce the actions of others. In addition to facilitating the transmission of knowledge and skills, it has been suggested that this fundamental cognitive capacity is at the origin of other human faculties ...

EPFL2008

Interferences in the Transformation of Reference Frames during a Posture Imitation Task

Aude Billard, Eric Sauser

We present a biologically-inspired neural model addressing the problem of transformations across frames of reference in a posture imitation task. Our modeling is based on the hypothesis that imitation is mediated by two concurrent transformations selective ...

2007

Parallel and Distributed Neural Models of the Ideomotor Principle: An Investigation of Imitative Cortical Pathways

Aude Billard, Eric Sauser

Humans’ capacity to imitate has been extensively investigated through a wide-range of behavioral and developmental studies. Yet, despite the huge amount of phenomenological evidence gathered, we are still unable to relate this behavioral data to any specif ...

2006

Biologically Inspired Multimodal Integration: Interferences in a Human-Robot Interaction Game

Aude Billard, Eric Sauser

This paper presents a biologically inspired approach to multimodal integration and decision-making in the context of human-robot interactions. More specifically, we address the principle of ideomotor compatibility by which observing the movements of others ...

2006

Dynamic Updating of Distributed Neural Representations using Forward Models

Aude Billard, Eric Sauser

In this paper, we present a continuous attractor network model, which we hypothesize will give some suggestion of the mechanisms underlying several neural processes, such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor-transform ...

2006

Three Dimensional Frames of References Transformations using Recurrent Populations of Neurons

Aude Billard, Eric Sauser

This work investigates whether population vector coding, a distributed computational paradigm, could be a principle mechanism for performing sensorimotor and frames of reference transformations. This paper presents a multilayer neural network that can perf ...

2005

View Sensitive Cells as a Neural Basis for the Representation of Others in a Self-Centered Frame of Reference

Aude Billard, Eric Sauser

This work follows from a research project, in which we investigate the underlying mechanisms of human imitation and develop a neural model of its core neural circuits. The present paper presents a model of a neural mechanism by which an imitator agent can ...

2005

Neural Model of the Transformation from Allo-centric to Ego- centric Representation of Motions

Aude Billard, Eric Sauser

2005

Coevolution of active vision and feature selection

Dario Floreano, Eric Sauser, Toshifumi Kato, Davide Marocco

We show that complex visual tasks, such as position- and size-invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a coevolutionary process of active vision and feature selection. Behavioral m ...

2004