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 humanrobot interaction communities is the design of teachable robots 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, and 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 interdisciplinary 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.
|Title of host publication||HRI 2023 - Companion of the ACM/IEEE International Conference on Human-Robot Interaction|
|Publisher||IEEE Computer Society|
|Number of pages||3|
|State||Published - 13 Mar 2023|
|Event||18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023 - Stockholm, Sweden|
Duration: 13 Mar 2023 → 16 Mar 2023
|Name||ACM/IEEE International Conference on Human-Robot Interaction|
|Conference||18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023|
|Period||13/03/23 → 16/03/23|
Bibliographical notePublisher Copyright:
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- Interactive robot learning
- Learning from human input
- Socially intelligent robots
- Socially interactive learning