Baskaran Prakash, Adams Julie A
Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR, United States.
Front Robot AI. 2023 Aug 7;10:1123374. doi: 10.3389/frobt.2023.1123374. eCollection 2023.
Human-robot teams collaborating to achieve tasks under various conditions, especially in unstructured, dynamic environments will require robots to adapt autonomously to a human teammate's state. An important element of such adaptation is the robot's ability to infer the human teammate's tasks. Environmentally embedded sensors (e.g., motion capture and cameras) are infeasible in such environments for task recognition, but wearable sensors are a viable task recognition alternative. Human-robot teams will perform a wide variety of composite and atomic tasks, involving multiple activity components (i.e., gross motor, fine-grained motor, tactile, visual, cognitive, speech and auditory) that may occur concurrently. A robot's ability to recognize the human's composite, concurrent tasks is a key requirement for realizing successful teaming. Over a hundred task recognition algorithms across multiple activity components are evaluated based on six criteria: sensitivity, suitability, generalizability, composite factor, concurrency and anomaly awareness. The majority of the reviewed task recognition algorithms are not viable for human-robot teams in unstructured, dynamic environments, as they only detect tasks from a subset of activity components, incorporate non-wearable sensors, and rarely detect composite, concurrent tasks across multiple activity components.
人机团队在各种条件下协作完成任务，尤其是在非结构化、动态环境中，这将要求机器人自主适应人类队友的状态。这种适应的一个重要因素是机器人推断人类队友任务的能力。在这样的环境中，用于任务识别的环境嵌入式传感器（如动作捕捉和摄像头）不可行，但可穿戴传感器是一种可行的任务识别替代方案。人机团队将执行各种各样的复合任务和原子任务，涉及多个可能同时发生的活动组件（即大肌肉运动、精细运动、触觉、视觉、认知、语音和听觉）。机器人识别人类复合并发任务的能力是实现成功协作的关键要求。基于六个标准对一百多种跨多个活动组件的任务识别算法进行了评估：灵敏度、适用性、通用性、复合因子、并发度和异常感知。大多数被审查的任务识别算法在非结构化、动态环境中对人机团队不可行，因为它们只从活动组件的一个子集中检测任务，纳入了非可穿戴传感器，并且很少检测跨多个活动组件的复合并发任务。
