Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with people effectively, robots must recognize human plans and goals from noisy observations of human actions, even when the user acts sub-optimally. Previous works on Plan and Goal Recognition (PGR) as planning have used hierarchical task networks (HTN) to model the actor/human. However, these techniques are insufficient as they do not have user engagement via natural modes of interaction such as language. Moreover, they have no mechanisms to let users, especially those with cognitive impairments, know of a deviation from their original plan or about any sub-optimal actions taken towards their goal. We propose a novel framework for plan and goal recognition in partially observable domains -- Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its belief in human progress by asking clarification questions about noisy sensor data and sub-optimal human actions. We evaluate the performance of D4GR over two simulated domains -- kitchen and blocks domain. With language feedback and the world state information in a hierarchical task model, we show that D4GR framework for the highest sensor noise performs 1% better than HTN in goal accuracy in both domains. For plan accuracy, D4GR outperforms by 4% in the kitchen domain and 2% in the blocks domain in comparison to HTN. The ALWAYS-ASK oracle outperforms our policy by 3% in goal recognition and 7%in plan recognition. D4GR does so by asking 68% fewer questions than an oracle baseline. We also demonstrate a real-world robot scenario in the kitchen domain, validating the improved plan and goal recognition of D4GR in a realistic setting.

翻译：会话式辅助机器人可帮助人群（特别是认知障碍者）完成烹饪、锻炼或操作机器等各类任务。然而，为有效与人交互，机器人必须能从含噪声的人类动作观测中识别用户计划与目标，即便用户行为存在次优性。以往将计划与目标识别（PGR）建模为规划问题的研究，多采用层次任务网络（HTN）模拟行为主体。但这类技术存在不足：既未能通过语言等自然交互模式实现用户参与，也缺少让用户（尤其是认知障碍者）了解其偏离原始计划或采取次优目标导向行为的机制。我们提出一种部分可观测域中的计划与目标识别新框架——面向目标识别的对话机制（D4GR），使机器人能通过询问关于含噪传感器数据与次优人类行为的澄清问题，修正对人类进度的信念。我们在模拟厨房域与积木域中评估D4GR性能。研究表明，将语言反馈与层次任务模型中的世界状态信息相结合后，D4GR框架在最高传感器噪声条件下，双域的目标识别准确率均比HTN提升1%。在计划识别准确率方面，相较HTN，D4GR在厨房域提升4%，在积木域提升2%。始终询问型预言机策略在目标识别与计划识别上分别比我们的策略高出3%与7%，但D4GR通过减少68%的提问量实现该性能。我们还展示了厨房域真实机器人场景，验证了D4GR在现实环境中对计划与目标识别的改进效果。