March 2023

2nd Workshop on Human-Interactive Robot Learning

at HRI'23

With robots poised to enter our daily environments, they will not only need to work for people, but also learn from them. An active area of investigation in the robotics, machine learning, and human-robot interaction communities is the design of teachable robotic agents that can learn interactively from human input. To refer to these research efforts, we use the umbrella term Human-Interactive Robot Learning (HIRL). While algorithmic solutions for robots learning from people have been investigated in a variety of ways, HIRL, as a fairly new research area, is still lacking:
1) a formal set of definitions to classify related but distinct research problems or solutions.
2) benchmark tasks, interactions, and metrics to evaluate the performance of HIRL algorithms and interactions.
3) clear long-term research challenges to be addressed by different communities.

Last year we began consolidating the needed definitions and vocabulary to enable fruitful discussions between researchers from these interdiciplinary fields, and identified a preliminary list of long, medium, and short-term research problems for the community to tackle, and existing tools and frameworks that can be leveraged to this end. This workshop will build upon these discussions, focusing on promoting the specification and design of HIRL benchmarks.

The topics of interest for this workshop include, but are not limited to:


Reuth Mirsky

The University of Texas at Austin and Bar Ilan University

Kim Baraka

Vrije Universiteit Amsterdam

Taylor Kessler Faulkner

The University of Texas at Austin

Justin Hart

The University of Texas at Austin

Harel Yedidsion

Applied Materials

Xuesu Xiao

X, The Moonshot Factory

Ifrah Idrees

Brown University

Ethan K. Gordon

University of Washington