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Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense crowds, surprising nearby pedestrians, or even causing collisions. While recent research on robot learning has shown promises in data-driven social robot navigation, good-quality training data is still difficult to acquire through either trial and error or expert demonstrations. In this work, we propose to utilize the body of rich, widely available, social human navigation data in many natural human-inhabited public spaces for robots to learn similar, human-like, socially compliant navigation behaviors. To be specific, we design an open-source egocentric data collection sensor suite wearable by walking humans to provide multimodal robot perception data; we collect a large-scale (~100 km, 20 hours, 300 trials, 13 humans) dataset in a variety of public spaces which contain numerous natural social navigation interactions; we analyze our dataset, demonstrate its usability, and point out future research directions and use cases.11Website: https://cs.gmu.edu/-xiao/Research/MuSoHu/
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Theoretical Considerations for Social Learning between a Human Observer and a Robot Model
Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.
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- Award ID(s):
- 1909847
- PAR ID:
- 10288682
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- ISSN:
- 1071-1813
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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