Human-Like Arm Motion Generation: A Review

Abstract

In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analysed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioural and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations.

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

“…The back-and-forth movement. Using equation (12) and the forward kinematics of the armhand, it is possible to predict collisions with the obstacles in the environment, the body of the robot, and the object to be grasped (in case of manipulation). To ensure the avoidance of collisions, a feasible movement, that still brings the arm-hand from the SP to a previously selected optimal TP, needs to be computed.…”

“…Most of the proposed techniques are global, rely on customized samplingbased planners, and often ignore typical human-like time parametrizations. 12 In global planning methods, like the proposed HUMP, collisions-free trajectories are generated before the beginning of a movement (off-line) by only considering sensory information coming from the workspace of a robot. Global methods (such as the samplingbased methods) also allow to integrate specific cost functions, which can be derived empirically from human motion data to generate movements with human-like peculiarities.…”

“…So far, most robotics implementations of human-like motion features were restricted to the end-effector motion in relatively simple point-to-point or planar movement paradigms. 12 Concerning human upper-limb movements, remarkably robust regularities have been identified that are not in any way implied by the task. A well-established example is the relationship between the geometrical properties of the motion path and movement speed.…”