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Designing and implementing human-robot interactions requires numerous skills, from having a rich understanding of social interactions and the capacity to articulate their subtle requirements, to the ability to then program a social robot with the many facets of such a complex interaction. Although designers are best suited to develop and implement these interactions due to their inherent understanding of the context and its requirements, these skills are a barrier to enabling designers to rapidly explore and prototype ideas: it is impractical for designers to also be experts on social interaction behaviors, and the technical challenges associated with programming a social robot are prohibitive. In this work, we introduce Synthé, which allows designers to act out, or bodystorm, multiple demonstrations of an interaction. These demonstrations are automatically captured and translated into prototypes for the design team using program synthesis. We evaluate Synthé in multiple design sessions involving pairs of designers bodystorming interactions and observing the resulting models on a robot. We build on the findings from these sessions to improve the capabilities of Synthé and demonstrate the use of these capabilities in a second design session.
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Learning Turn-Taking Behavior from Human Demonstrations for Social Human-Robot Interactions
Turn-taking is a fundamental behavior during human interactions and robots must be capable of turn-taking to interact with humans. Current state-of-the-art approaches in turn-taking focus on developing general models to predict the end of turn (EoT) across all contexts. This demands an all-inclusive verbal and non-verbal behavioral dataset from all possible contexts of interaction. Before robot deployment, gathering such a dataset may be infeasible and/or impractical. More importantly, a robot needs to predict the EoT and decide on the best time to take a turn (i.e, start speaking). In this research, we present a learning from demonstration (LfD) system for a robot to learn from demonstrations, after it has been deployed, to make decisions on the appropriate time for taking a turn within specific social interaction contexts. The system captures demonstrations of turn-taking during social interactions and uses these demonstrations to train a LSTM RNN based model to replicate the turn-taking behavior of the demonstrator. We evaluate the system for teaching the turn-taking behavior of an interviewer during a job interview context. Furthermore, we investigate the efficacy of verbal, prosodic, and gestural cues for deciding when to begin a turn.
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- Award ID(s):
- 1948224
- PAR ID:
- 10442501
- Date Published:
- Journal Name:
- 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Page Range / eLocation ID:
- 7643 to 7649
- Format(s):
- Medium: X
- 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/10.1109/IROS47612.2022.9981243
