NOPA: Neurally-guided Online Probabilistic Assistance
for Building Socially Intelligent Home Assistants

Xavier Puig*
Tianmin Shu*
Joshua B. Tenenbaum
Antonio Torralba
MIT
(* Equal contribution)
[Paper]
[GitHub]

Abstract

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' 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'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.


Video



Paper

X. Puig*, T. Shu*, J. B. Tenenbaum, A. Torralba
NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants. IEEE International Conference on Robotics and Automation (ICRA), 2023
Paper




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