Workshop on Behavior Adaptation, Interaction and Learning for Assistive Robotics

A full-day workshop hosted at IEEE RO-MAN 2016, NYC, USA

With robots getting out of the cages, Human-Robot Interaction applications effectiveness has not only to rely on the skills of trained users, but also on the ability of the robot to adapt to the users’ behavior and needs as well. In particular, the development of personal robots, as assistive technological tools, challenges researchers to develop socially intelligent and adaptive robots that can collaborate with people.

Personal robots are expected to incrementally learn user preferences and to modify and adapt their behaviors accordingly. Indeed, for improved and natural human-robot cooperation, human users will learn how to interact with the robot but, at the time, the robotic systems should adapt to the users. This adaptation requires learning a model of human behavior and integrating this model into the decision-making algorithm of the robot. Creating robotic systems capable of correctly model and recognize the human behavior and of adapting their behavior to the user is a very critical task, especially in the domain of assistive robotics and when working with vulnerable user populations.

Intended Audience

This workshop is intended as a forum for a broad audience, which spans from machine learning to user profiling and robot behavior control, and it is a place to exchange opinions, to discuss innovative ideas and to get hints and suggestions on ongoing researches.


    BAILAR is now scheduled as a full-day workshop

    Invited Speakers Announced:
  • Heni Ben Amor,Arizona State University - ASU Interactive Robotics Laboratory
  • Brian Scassellati, Yale University - NSF Expedition on Socially Assistive Robotics