AI and Its Alternatives in Assistive and Collaborative Robotics

RSS 2019 Workshop

Shared autonomy is a critical component of human-robot interaction (HRI) that allows robots to collaborate with and provide intuitive assistance to human partners. It is an interdisciplinary domain that brings together the science of human behaviors with state-of-the-art methods in engineering, such as artificial intelligence (AI). Unfortunately, there is often a disconnect between researchers that explore the human side of these interactions and those that propose new engineering solutions. In the former, researchers study human psychology, neuroscience, and biomechanics to better understand how the human partner thinks and functions. In the latter, researchers develop autonomous technologies to improve the capabilities of the human-in-the-loop, often ignoring important facets like perceived utility and acceptance. In this workshop, we bring together experts from both perspectives to define and address challenges in designing and implementing shared autonomy solutions. We will ask questions like — is it possible to create a reliable model of human intent? Can we design interpretable AI to better facilitate HRI? How do we incorporate formal notions of safety with data-driven methods? And finally, how do we take learnings from psychology, neuroscience, and self-driving cars to create better assistive technologies? This workshop will foster multidisciplinary discussion and friendly debate as well as consolidate perspectives, methodologies, and assessment tools to grow research efforts in human-centered robotics.

Discussion Topics

Session 1 | Sharing Control:

How do we efficiently incorporate learnings from biomechanics and neuroscience into shared control paradigms? Can we create interpretable AI that takes advantage of that knowledge? When should autonomy over-ride the human versus the human overriding autonomy? Should we make decisions based on a worst-case analysis or based on the most likely outcome? Can we guarantee safety while using learning methods? And finally, what can we learn from shared autonomy experiments in self-driving cars?

Session 2 | Inferring Intent:

Is it possible to create a reliable model of human intent? What sensory information is most useful? Is there a shared responsibility for communication to establish bidirectional trust? Are personal models beneficial? Do we need “personal” models, or just “adaptive” models?

Invited Speakers

Etienne Burdet

Imperial

Jim Mainprice

University of Stuttgart

Michael Gleicher

University of Wisconsin

Brandon Northcutt

Toyota Research Institute

Aude Billard

EPFL

Anca Dragan

UC Berkeley

Agnieszka Wykowska

Istituto Italiano di Tecnologia

Heni Ben Amor

Arizona State University

Brenna Argall

Northwestern University

Schedule Overview

08:50-09:00 : Welcome

Morning Session | Sharing Control

09:00AM-09:30AM : Invited Speaker09:30AM-10:00AM : Invited Speaker10:00AM-10:30AM : Invited Speaker10:30AM-10:45AM : Coffee Break10:45AM-11:15AM : Invited Speaker11:15AM-11:45AM : Panel Discussion
11:45AM-12:15PM : Poster Session 112:15PM-01:00PM : Lunch01:00PM-01:30PM : Spotlight Presentations

Afternoon Session | Inferring Intent

01:30PM-02:00PM : Invited Speaker02:00PM-02:30PM : Invited Speaker02:30PM-02:45PM : Coffee Break02:45PM-03:15PM : Invited Speaker03:15PM-03:45PM : Invited Speaker03:45PM-04:15PM : Invited Speaker
04:15PM-04:45PM : Panel Discussion04:45PM-05:00PM : Closing Discussion and Wrap-Up05:00PM-05:30PM : Poster Session 2

Important Dates

Submission deadline (AoE time) June 10

Notification of acceptance June 14

Camera-ready deadline June 19

Workshop June 23

Call for Abstracts

We solicit extended abstracts for peer review. Abstracts should conform to RSS style guidelines and should be a maximum of 4 pages (excluding references). Submissions can include archived or previously accepted work (please make a note of this in the submission). Reviewing will be single blind.

Submission link: https://easychair.org/my/conference?conf=rss19aiacr

Topics of interest include, but are not limited to:

  • Shared autonomy / Shared control / Human-in-the-loop systems
  • The intersection of AI and psychology/neuroscience for HRI
  • Data-driven models of the human and/or autonomous partner for HRI
  • Interpretable AI for HRI
  • Theory of mind for HRI
  • Formal notions of safety in HRI
  • Assistive robotics
  • Collaborative robotics
  • Rehabilitation robotics
  • Human-oriented autonomy

All accepted contributions will be presented in interactive poster sessions.