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This work studies the problem of predicting human intent to interact with a robot in a public environment. To facilitate research in this problem domain, we first contribute the People Approaching Robots Database (PAR-D), a new collection of datasets for intent prediction in Human-Robot Interaction. The database includes a subset of the ATC Approach Trajectory dataset [28] with augmented ground truth labels. It also includes two new datasets collected with a robot photographer on two locations of a university campus. Then, we contribute a novel human-annotated baseline for predicting intent. Our results suggest that the robot’s environment and the amount of time that a person is visible impacts human performance in this prediction task. We also provide computational baselines for intent prediction in PAR-D by comparing the performance of several machine learning models, including ones that directly model pedestrian interaction intent and others that predict motion trajectories as an intermediary step. From these models, we find that trajectory prediction seems useful for inferring intent to interact with a robot in a public environment.
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Intent Detection with WikiHow
Modern task-oriented dialog systems need to reliably understand users’ intents. Intent detection is even more challenging when moving to new domains or new languages, since there is little annotated data. To address this challenge, we present a suite of pretrained intent detection models which can predict a broad range of intended goals from many actions because they are trained on wikiHow, a comprehensive instructional website. Our models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our models also demonstrate strong zero- and few-shot performance, reaching over 75% accuracy using only 100 training examples in all datasets.
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- Award ID(s):
- 1928474
- PAR ID:
- 10344235
- Date Published:
- Journal Name:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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