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Justin Hart

4 accepted papers

2021

Watch Where You’re Going! Gaze and Head Orientation as Predictors for Social Robot Navigation

ICRA 2021poster

Mobile robots deployed in human-populated environments must be able to safely and comfortably navigate in close proximity to people. Head orientation and gaze are both mechanisms which help people to interpret where other people intend to walk, which in turn enables them to coordinate their movement…

Cited by 23SourceScholar
2019

Improving Grounded Natural Language Understanding through Human-Robot Dialog

ICRA 2019poster

Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept word…

Cited by 85SourcecodeScholar
2017

Opportunistic Active Learning for Grounding Natural Language Descriptions

CoRL 2017

Active learning identifies data points from a pool of unlabeled examples whose labels, if made available, are most likely to improve the predictions of a supervised model. Most research on active learning assumes that an agent has access to the entire pool of unlabeled data and can ask for labels of

Cited by 0SourcePDFScholar