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In previous work, researchers have repeatedly demonstrated that robots' use of deictic gestures enables effective and natural human-robot interaction. However, new technologies such as augmented reality head mounted displays enable environments in which mixed-reality becomes possible, and in such environments, physical gestures become but one category among many different types of mixed reality deictic gestures. In this paper, we present the first experimental exploration of the effectiveness of mixed reality deictic gestures beyond physical gestures. Specifically, we investigate human perception of videos simulating the display of allocentric gestures, in which robots circle their targets in users' fields of view. Our results suggest that this is an effective communication strategy, both in terms of objective accuracy and subjective perception, especially when paired with complex natural language references.
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Interactively Explaining Robot Policies to Humans in Integrated Virtual and Physical Training Environments
Policy summarization is a computational paradigm for explaining the behavior and decision-making processes of autonomous robots to humans. It summarizes robot policies via exemplary demonstrations, aiming to improve human understanding of robotic behaviors. This understanding is crucial, especially since users often make critical decisions about robot deployment in the real world. Previous research in policy summarization has predominantly focused on simulated robots and environments, overlooking its application to physically embodied robots. Our work fills this gap by combining current policy summarization methods with a novel, interactive user interface that involves physical interaction with robots. We conduct human-subject experiments to assess our explanation system, focusing on the impact of different explanation modalities in policy summarization. Our findings underscore the unique advantages of combining virtual and physical training environments to effectively communicate robot behavior to human users.
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- Award ID(s):
- 2222876
- PAR ID:
- 10517599
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
- ISBN:
- 9798400703232
- Page Range / eLocation ID:
- 847 to 851
- Subject(s) / Keyword(s):
- Robotics Explainable AI Value Alignment AI-Assisted Human Training
- Format(s):
- Medium: X
- Location:
- Boulder CO USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3610978.3640656
