Please use this identifier to cite or link to this item: https://doi.org/10.15607/rss.2022.xviii.020
Title: MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication
Authors: Chen, Kaiqi
Fong, Jeffrey
Soh, Harold 
Issue Date: 27-Jun-2022
Publisher: Robotics: Science and Systems Foundation
Citation: Chen, Kaiqi, Fong, Jeffrey, Soh, Harold (2022-06-27). MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication. Robotics: Science and Systems 2022. ScholarBank@NUS Repository. https://doi.org/10.15607/rss.2022.xviii.020
Abstract: Communication is a hallmark of intelligence. In this work, we present MIRROR, an approach to (i) quickly learn human models from human demonstrations, and (ii) use the models for subsequent communication planning in assistive shared-control settings. MIRROR is inspired by social projection theory, which hypothesizes that humans use self-models to understand others. Likewise, MIRROR leverages self-models learned using reinforcement learning to bootstrap human modeling. Experiments with simulated humans show that this approach leads to rapid learning and more robust models compared to existing behavioral cloning and state-of-the-art imitation learning methods. We also present a human-subject study using the CARLA simulator which shows that (i) MIRROR is able to scale to complex domains with high-dimensional observations and complicated world physics and (ii) provides effective assistive communication that enabled participants to drive more safely in adverse weather conditions.
Source Title: Robotics: Science and Systems 2022
URI: https://scholarbank.nus.edu.sg/handle/10635/230578
DOI: 10.15607/rss.2022.xviii.020
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