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.

本研究探讨如何构建社交智能机器人以协助人们在家中。特别关注在线目标推断方面的辅助，在这种情况下，机器人必须同时推断人类的目标及如何帮助他们实现这些目标。我们的 NOPA 方法解决了自适应性和规模性方面的挑战，该方法融合了神经元目标提案和反向规划，以及粒子滤波，以在不确定性条件下实现鲁棒推断。我们将 NOPA 与多个基线在新的“在线观看和协助”挑战中进行比较，实验表明，我们的辅助智能体能够稳健地更新其目标推断并适应不断变化的不确定性水平。

NOPA: 构建具有社交智能的智能家居助手的神经引导在线概率辅助