Robot assistants for older adults and people with disabilities need to interact with their users in collaborative tasks. The core component of these systems is an interaction manager whose job is to observe and assess the task, and infer the state of the human and their intent to choose the best course of action for the robot. Due to the sparseness of the data in this domain, the policy for such multi-modal systems is often crafted by hand; as the complexity of interactions grows this process is not scalable. In this paper, we propose a reinforcement learning (RL) approach to learn the robot policy. In contrast to the dialog systems, our agent is trained with a simulator developed by using human data and can deal with multiple modalities such as language and physical actions. We conducted a human study to evaluate the performance of the system in the interaction with a user. Our designed system shows promising preliminary results when it is used by a real user.

翻译：面向老年人和残障人士的机器人辅助系统需在协作任务中与用户进行互动。此类系统的核心组件是交互管理器，其职责是观察并评估任务状态，推断人类状态及意图，从而为机器人选择最优行动方案。由于该领域数据稀疏性，此类多模态系统的策略常通过人工制定；随着交互复杂度的增长，这种流程将难以扩展。本文提出一种基于强化学习（RL）的机器人策略学习方法。与对话系统不同，我们的智能体通过人类数据训练的仿真器进行训练，并能处理语言和物理动作等多模态信息。我们开展了一项用户研究，评估系统与人交互时的表现。实际用户测试结果表明，所设计系统展现出初步的可观性能。