Examining complex cognitive-motor performance in humanoid robots and humans can inform their interactions in a social context of team dynamics. Namely, the understanding of human cognitive-motor control and learning mechanisms can inform human motor behavior and also the development of intelligent controllers for robots when interacting with people. While prior humans and humanoid robot studies mainly examined motion planning, only a few have investigated high-level motor planning underlying action sequences for complex task execution. This sparse work has largely considered well-constrained problems using fairly simple performance assessment methods without detailed action sequence analyses. Here we qualitatively and quantitatively assess action sequences generated by humans and a humanoid robot during execution of two tasks providing various challenge levels and learning paradigms while offering flexible success criteria. The Levenshtein distance and its operators are adapted to the motor domain to provide a detailed performance assessment of action sequences by comparing them to a reference sequence (perfect sequence having a minimal number of actions). The results reveal that (i) humans produced a large variety of action sequences combining perfect and imperfect sequences while still reaching the task goal, whereas the robot generated perfect/near-perfect successful action sequences; (ii) the Levenshtein distance and the number of insertions provide reliable performance markers capable of differentiating perfect and imperfect sequences; (iii) the deletion operator is the most sensitive marker of action sequence failure. This work complements prior efforts for complex task performance assessment in humans and humanoid robots and has the potential to inform human–machine interactions.

在类人动态的社交环境中，研究类人机器人和人类的复杂认知运动表现可以为他们的互动提供信息。即，对人类认知运动控制和学习机制的理解可以指导人类运动行为，以及与人互动时机器人智能控制器的发展。虽然先前的人类和类人机器人研究主要研究运动计划，但只有少数研究了用于复杂任务执行的基础运动计划的高级运动计划。这项稀疏的工作使用相当简单的性能评估方法就很大程度上考虑了约束良好的问题，而没有进行详细的动作序列分析。在这里，我们定性和定量地评估了人类和类人机器人在执行两项任务期间产生的动作序列，这两项任务提供了不同的挑战级别和学习范例，同时提供了灵活的成功标准。Levenshtein距离及其算子适用于运动域，通过将动作序列与参考序列（具有最少动作次数的完美序列）进行比较，来提供动作序列的详细性能评估。结果表明：（i）人类在达成任务目标的同时产生了完美和不完美序列的多种动作序列，而机器人则产生了完美/近乎完美的成功动作序列；（ii）Levenshtein距离和插入次数提供了可靠的性能指标，能够区分完美和不完美的序列；（iii）删除操作员是动作序列失败的最敏感标志。这项工作是对人类和类人机器人中复杂任务性能评估的先前工作的补充，并且具有为人机交互提供信息的潜力。