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Amal Nanavati (University of Washington), Christoforos Mavrogiannis (University of Washington), Kevin Weatherwax (UC Santa Cruz), Leila Takayama (UC Santa Cruz), Maya Cakmak (University of Washington), Siddhartha Srinivasa (University of Washington) |
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| Paper #016 |
| Interactive Poster Session II | Interactive Poster Session VII |
Robots deployed in human-populated spaces often need human help to effectively complete their tasks. Yet, a robot that asks for help too frequently or at the wrong times may cause annoyance, and a robot that asks too infrequently may be unable to complete its tasks. In this paper, we present a model of humans’ helpfulness towards a robot in an office environment, learnt from online user study data. Our key insight is that effectively planning for a task that involves bystander help requires disaggregating individual and contextual factors and explicitly reasoning about uncertainty over individual factors. Our model incorporates the individual factor of latent helpfulness and the contextual factors of human busyness and robot frequency of asking. We integrate the model into a Bayes-Adaptive Markov Decision Process (BAMDP) framework and run a user study that compares it to baseline models that do not incorporate individual or contextual factors. The results show that our model significantly outperforms baseline models by a factor of 1.5X, and it does so by asking for help more effectively: asking 1.2X times less while still receiving more human help on average.
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