Posts by Collection

Biped humanoid robot locomotion

Learning impedance controllers from demonstrations

Human-robot interaction for children handwriting acquisition - CoWriter

Robot learning with priors based on latent variables

Learning Object-level Impedance Control for Robust Grasping and Dexterous Manipulation

Published in ICRA, 2014

This paper is about learning an impedance behaviour of an object manipulated by a multi-fingered hand, which is based on a virtual frame estimation based on tactile sensors.

Recommended citation: M. Li, H. Yin, K. Tahara and A. Billard, Learning Object-level Impedance Control for Robust Grasping and Dexterous Manipulation, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, 2014

Learning Cost Function and Trajectory for Robotic Writing Motion

Published in HUMANOIDS, 2014

This paper is about probabilistic inference approach for learning cost function and trajectories, developing compliant motion for robotic handwriting.

Recommended citation: H. Yin, A. Paiva and A. Billard, Learning Cost Function and Trajectory for Robotic Writing Motion, In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), Madrid, Spain, 2014

Bidirectional Learning of Handwriting Skill in Human-Robot Interaction

Published in HRI Pioneers, 2015

This paper is about a framework that enables bidirectional learning of handwriting skills betwee children and robots in human-robot interaction activities.

Recommended citation: H. Yin, A. Billard and A. Paiva, Bidirectional Learning of Handwriting Skill in Human-Robot Interaction, In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). HRI Pioneer Workshop, Portland, USA, 2015.

Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations

Published in IJCAI, 2016

This paper is about modeling multi-mode motion to generate diverse human-like handwriting motion.

Recommended citation: H. Yin, P. Alves-Olivera, F. S. Melo, A. Billard and A. Paiva, Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations, In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), New York, USA, 2016

Associate Latent Encodings in Learning from Demonstrations

Published in AAAI, 2017

This paper is about imitation learning that allows to learn features and associativity from multi-modal demonstrations.

Recommended citation: H. Yin, F. S. Melo, A. Billard and A. Paiva, Associate Latent Encodings in Learning from Demonstrations, In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI). San Francisco, California, USA, 2017.

Affect of Robot’s Competencies on Children’s Perception

Published in AAMAS, 2017

This paper is about an HRI human study showing the learning capability of robots will not impact their social relationship with children.

Recommended citation: S. Chandra, R. Paradeda, H. Yin, P. Dillenbourg, R. Prada and A. Paiva, Affect of robot’s competencies on children’s perception, In Proceedings of the International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). São Paulo, Brazil, 2017.

Do Children Perceive Whether a Robotic Peer is Learning or Not

Published in HRI, 2018

This paper is about an HRI system and human study showing the learning capability of robots can impact children learning gains after multi-session interactions.

Recommended citation: S. Chandra, R. Paradeda, H. Yin, P. Dillenbourg, R. Prada and A. Paiva, Do Children Perceive Whether a Robotic Peer is Learning or Not, In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). Chicago, USA, 2018.

An Ensemble Inverse Optimal Control Approach for Robotic Task Learning and Adaptation

Published in Autonomous Robots, 2018

This paper is about learning aggregated task objective functions to encode multi-mode task behaviors and their adaptation.

Recommended citation: H. Yin, F. S. Melo, A. Paiva and A. Billard, An Ensemble Inverse Optimal Control Approach for Robotic Task Learning and Adaptation, Autonomous Robots, 2018.

Boosting Robot Learning and Control with Domain Constraints

Published in RSS Pioneers, 2018

This paper is about expressing and incorporating task priors as latent variables to boost robot motion performance in different scenarios.

Recommended citation: H. Yin, F. S. Melo, A. Billard and A. Paiva, Boosting Robot Learning and Control with Domain Constraints, Robotics: Science and Systems (RSS), RSS Pioneer Workshop. Pittsburgh, USA, 2018.

Learning Cost Function and Trajectory for Robotic Writing Motion

Published: November 20, 2014

Oral presentation for our paper accepted to the venue.

Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations

Published: July 13, 2016

Oral presentation for our paper accepted to the venue.

Associate Latent Encodings in Learning from Demonstrations

Published: February 08, 2017

Oral presentation for our paper accepted to the venue.

Human Priors in Robot Motion Learning and Synthesis

Published: December 26, 2017

Invited talk on the work of exploring and incorporating prior task knowledge in learning and synthesizing robot motion.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.