@conference {5545,
	title = {NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants},
	booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2023},
	month = {07/2023},
	address = {London, United Kingdom},
	abstract = {<p>In this work, we study how to build socially intelligent robots to assist people in their homes. In particular, we focus on assistance with online goal inference, where robots must simultaneously infer humans{\textquoteright} goals and how to help them achieve those goals. Prior assistance methods either lack the adaptivity to adjust helping strategies (i.e., when and how to help) in response to uncertainty about goals or the scalability to conduct fast inference in a large goal space. Our NOPA (Neurally-guided Online Probabilistic Assistance) method addresses both of these challenges. NOPA consists of (1) an online goal inference module combining neural goal proposals with inverse planning and particle filtering for robust inference under uncertainty, and (2) a helping planner that discovers valuable subgoals to help with and is aware of the uncertainty in goal inference. We compare NOPA against multiple baselines in a new embodied AI assistance challenge: Online Watch-And-Help, in which a helper agent needs to simultaneously watch a main agent{\textquoteright}s action, infer its goal, and help perform a common household task faster in realistic virtual home environments. Experiments show that our helper agent robustly updates its goal inference and adapts its helping plans to the changing level of uncertainty. <sup>1</sup> <sup>1</sup> Code and a supplementary video are available at https://www.tshu.io/online_watch_and_help.</p>
},
	doi = {10.1109/ICRA48891.2023.10161352},
	url = {https://ieeexplore.ieee.org/document/10161352/},
	author = {Puig, Xavier and Shu, Tianmin and Tenenbaum, Joshua B. and Torralba, Antonio}
}
@conference {5291,
	title = {Incorporating Rich Social Interactions Into MDPs},
	booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)2022 International Conference on Robotics and Automation (ICRA)},
	year = {2022},
	month = {05/2022},
	address = {Philadelphia, PA, USA},
	abstract = {<p>Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology can be formalized by extending a nested MDP where agents reason about arbitrary functions of each other\&amp;$\#$x0027;s rewards. This extended Social MDP allows us to encode the five basic interactions that underlie microsociology: cooperation, conflict, coercion, competition, and exchange. The result is a robotic agent capable of executing social interactions in new environments with no interaction-specific training; like humans it can engage socially in novel ways even without a single example of that social interaction. Moreover, the estimations of these Social MDPs align closely with the judge-ments of humans when considering which social interaction is taking place in an environment. This method both sheds light on the nature of social interactions, by providing concrete mathematical definitions, and brings rich social interactions into a mathematical framework that has proven to be natural for robotics.</p>
},
	doi = {10.1109/ICRA46639.2022.9811991},
	url = {https://ieeexplore.ieee.org/document/9811991/},
	author = {Tejwani, Ravi and Kuo, Yen-Ling and Shu, Tianmin and Stankovits, Bennett and Gutfreund, Dan and Tenenbaum, Joshua B. and Katz, Boris and Barbu, Andrei}
}
