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In this work, we study how to build socially intelligent robots to assistpeople in their homes. In particular, we focus on assistance with online goalinference, where robots must simultaneously infer humans' goals and how to helpthem achieve those goals. Prior assistance methods either lack the adaptivityto adjust helping strategies (i.e., when and how to help) in response touncertainty about goals or the scalability to conduct fast inference in a largegoal space. Our NOPA (Neurally-guided Online Probabilistic Assistance) methodaddresses both of these challenges. NOPA consists of (1) an online goalinference module combining neural goal proposals with inverse planning andparticle filtering for robust inference under uncertainty, and (2) a helpingplanner that discovers valuable subgoals to help with and is aware of theuncertainty in goal inference. We compare NOPA against multiple baselines in anew embodied AI assistance challenge: Online Watch-And-Help, in which a helperagent needs to simultaneously watch a main agent's action, infer its goal, andhelp perform a common household task faster in realistic virtual homeenvironments. Experiments show that our helper agent robustly updates its goalinference and adapts its helping plans to the changing level of uncertainty.

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