A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots. (31st March 2021)

• Record Type: Journal Article
• Authors: Gulletta, Gianpaolo; Silva, Eliana Costa e; Erlhagen, Wolfram; Meulenbroek, Ruud; Costa, Maria Fernanda Pires; Bicho, Estela
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 2(2021)
• Publication Date: 2021-03-31
• Subjects: Humanoids and human-like robotics -- human-like motion planning -- cognitive systems -- human–robot interaction -- naturalistic obstacles-avoidance; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881421998585
• Languages: English
• ISSNs: 1729-8806

Abstract

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, theH uman-likeU pper-limbM otionP lanner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.