A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

 

The 31st of March 2021 a Human-like Upper-limb Motion Planner (HUMP) is released as a framework for the generation of arm-hand trajectories in humanoid robots. This is the first planner that addresses the avoidance of obstacles in a quantitatively assessed human-like manner and that does not rely on any database of recorded human arm motion demonstrations. This planner finds applications in typical assembly and manufacturing scenarios where a fluent and smooth interaction with human peers is desired. However, it can also be a support in the fabrication of intelligent upper-limb prosthesis that can replicate human-like movements for pick, place and reaching tasks.  

I am very happy to announce the publication of this article, which collects the results of experiments that have been carried on for several years. I hope this planner will significantly contribute to field of human-centered robotics and will be adopted by robotics developers who work on humanoid platforms.   

The abstract of the article is the following:

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, the Human-like Upper-limb Motion Planner, 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.

DOI: 10.1177/1729881421998585

You can find the implementation of the HUMP here: https://github.com/zohannn/HUMP