A key challenge in human-robot shared workspace is defining the decision criterion to select the next task for a fluent, efficient and safe collaboration. While working with robots in an industrial environment, tasks may comply with precedence constraints to be executed. A typical example of precedence constraint in industry occurs at an assembly station when the human cannot perform a task before the robot ends on its own. This paper presents a methodology based on the Maximum Entropy Inverse Optimal Control for the identification of a probability distribution over the human goals, packed into a software tool for human-robot shared-workspace collaboration. The software analyzes the human goal and the goal precedence constraints, and it is able to identify the best robot goal along with the relative motion plan. The approach used is, an algorithm for the management of goal precedence constraints and the Partially Observable Markov Decision Process (POMDP) for the selection of the next robot action. A comparison study with 15 participants was carried out in a real world assembly station. The experiment focused on evaluating the task fluency, the task efficiency and the human satisfaction. The presented model displayed reduction in robot idle time and increased human satisfaction.

人机共享工作空间中的一个关键挑战是定义决策标准，以选择下一个任务以实现流畅，高效和安全的协作。在工业环境中使用机器人工作时，任务可能符合要执行的优先级约束。工业界优先约束的典型示例发生在装配站，这是因为人们无法在机器人自行结束之前执行任务。本文提出了一种基于最大熵逆最优控制的方法，该方法用于识别人类目标上的概率分布，并将其打包到用于人机共享工作空间协作的软件工具中。该软件分析人类目标和目标优先约束，并且能够识别最佳机器人目标以及相对运动计划。使用的方法是 一种算法，用于管理目标优先约束和部分可观察的马尔可夫决策过程（POMDP），以选择下一个机器人动作。在一个现实世界的装配站中与15名参与者进行了比较研究。实验的重点是评估任务的流畅程度，任务效率和人类满意度。提出的模型显示减少了机器人的空闲时间并提高了人类满意度。