对话式辅助机器人可以帮助人们，尤其是认知障碍者，完成做饭、锻炼或操作机器等各种任务。然而，为了与人有效互动，机器人必须从对人类动作的含噪观测中识别人类计划和目标，即使用户采取次优行动也是如此。以往将计划与目标识别（PGR）视为规划问题的研究使用分层任务网络（HTN）来对行动者/人类建模。但是，这些技术存在不足，因为它们没有通过语言等自然交互方式促使用户参与。此外，它们也没有机制让用户，尤其是认知障碍者，知道自己偏离了原始计划，或知道自己为实现目标采取了任何次优行动。我们提出了一种用于部分可观测领域中计划与目标识别的新颖框架——Dialogue for Goal Recognition（D4GR），使机器人能够通过提出关于含噪传感器数据和次优人类行动的澄清问题，来修正其对人类进展的信念。我们在厨房和积木世界两个模拟领域中评估了D4GR的性能。通过在分层任务模型中结合语言反馈和世界状态信息，我们表明，在最高传感器噪声条件下，D4GR框架在两个领域中的目标准确率均比HTN高1%。在计划准确率方面，与HTN相比，D4GR在厨房领域高4%，在积木领域高2%。ALWAYS-ASK预言机在目标识别上比我们的策略高3%，在计划识别上高7%。D4GR通过比预言机基线少问68%的问题实现了上述性能。我们还展示了厨房领域中的真实机器人场景，验证了D4GR在现实环境中改进的计划与目标识别能力。
英文摘要：
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.
MindEye-OmniAssist: A Gaze-Driven LLM-Enhanced Assistive Robot System for Implicit Intention Recognition and Task Execution
Hubei Key Laboratory of Brain-Inspired Intelligent Systems, Huazhong University of Science and Technology（脑启发智能系统湖北省重点实验室，华中科技大学）；Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（图像处理与智能控制教育部重点实验室，人工智能与自动化学院，华中科技大学）；state key laboratory of intelligent vehicle safety technology, chongqing changan automobile co ltd（智能车辆安全技术 state key laboratory，重庆长安汽车有限公司）；Science and technology innovation center, China ship development and design centre（科技创新中心，中国船舶工业设计研究中心）
Utilizing Vision-Language Models as Action Models for Intent Recognition and Assistance
University of Birmingham（伯明翰大学）；Queen Mary University of London（伦敦大学女王学院）；Aston University（阿斯顿大学）；United Kingdom National Nuclear Laboratory Ltd.（英国国家核实验室有限公司）
Adaptive Motion Planning via Contact-Based Intent Inference for Human-Robot Collaboration
Department of Mechanical Engineering, Texas A&M University（机械工程系，德克萨斯A&M大学）；Department of Civil and Environmental Engineering, Texas A&M University（土木与环境工程系，德克萨斯A&M大学）
Unraveling the Connection: How Cognitive Workload Shapes Intent Recognition in Robot-Assisted Surgery
Cognitive Assistants（认知助手）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland Informatics Campus, Saarbrucken, Germany（萨尔布吕肯德国信息学校区）
Human Intention Recognition in Flexible Robotized Warehouses based on Markov Decision Processes
FER（萨格勒布大学电气工程与计算机学院）
电子商务的快速增长增加了对大型仓库及其自动化的需求，因此使用机器人作为人类工人的助手成为优先事项。为了有效和安全地操作，机器人助手或监督系统应该识别人类的意图。心理理论（Theory of Mind，ToM）是对其他主体心理状态的直观概念，即，信仰和欲望，以及它们如何导致行为。在本文中，我们提出了一个基于ToM的算法，在灵活的机器人仓库的人类意图识别。我们将仓库工人置于模拟的2D环境中，其中有三个潜在目标。我们观察代理的行动，并验证他们的目标位置使用马尔可夫决策过程框架。然后，这些观察结果由所提出的隐马尔可夫模型框架进行处理，该框架估计代理的愿望。我们证明了所提出的框架预测人类仓库工人的愿望在一个直观的方式，并在最后，我们讨论了模拟结果。
Human Intention Recognition for Human Aware Planning in Integrated Warehouse Systems
University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（布拉格捷克理工大学捷克信息学、机器人与控制论研究所）
Tactile-Based Human Intent Recognition for Robot Assistive Navigation
Electrical & Computer Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校电气与计算机工程系）；Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）
