High-Level Motor Planning Assessment During Performance of Complex Action Sequences in Humans and a Humanoid Robot

Abstract

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.