Situated Human–Robot Collaboration: predicting intent from grounded natural language
| Autor: | Sarah Widder, Jake Brawer, Alessandro Roncone, Olivier Mangin, Brian Scassellati |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Context model
Teamwork Computer science media_common.quotation_subject 05 social sciences Context (language use) 02 engineering and technology Autonomous robot Action selection Human–robot interaction Task (project management) Human–computer interaction 0202 electrical engineering electronic engineering information engineering Task analysis Domain knowledge 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences 050107 human factors Utterance Natural language media_common |
| Zdroj: | IROS |
| Popis: | Research in human teamwork shows that a key element of fluid and fluent interactions is the interpretation of implicit verbal and non-verbal cues in context. This poses an issue to robotic platforms, however, as they have historically worked best when controlled through explicit commands that have employed structured, unequivocal representations of the external world and their human partners. In this work, we present a framework for effectively grounding situated and naturalistic speech to action selection during human-robot collaborative activities. This is accomplished by maintaining and incrementally updating separate “speech” and “context” models that jointly classify a collaborator's utterance. We evaluate the efficacy of the system on a collaborative construction task with an autonomous robot and human participants. We first demonstrate that our system is capable of acquiring and deploying new task representations from limited and naturalistic data sets, and without any prior domain knowledge of language or the task itself. Finally, we show that our system is capable of significantly improving performance on an unfamiliar task after a one-shot exposure. |
| Databáze: | OpenAIRE |
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