Toward Understanding of Human Motion from Motor Control of Humanoid Robots

AbstractIn the last years of research in cognitive control, neuroscience and humanoid robotics have converged to different frameworks which aim, on one side, at modeling and analyzing human motion, and, on the other side, at enhancing motor abilities of humanoids. In this paper we try to cover the gap between the two areas, giving an overview of the literature in the two fields which concerns the production of movements. First, we survey computational motor control models based on optimality principles; then, we review available implementations and techniques to transfer these principles to humanoid robots, with a focus on the limitations and possible improvements of the current implementations. Moreover, we propose Stochastic Optimal Control as a framework to take into account delays and noise, thus catching the unpredictability aspects typical of both humans and humanoids systems. Optimal Control in general can also easily be integrated with Machine Learning frameworks, thus resulting in a computational implementation of human motor learning. This survey is mainly addressed to roboticists attempting to implement human-inspired controllers on robots, but can also be of interest for researchers in other fields, such as computational motor control.

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

International Journal of Advanced Robotic Systems ◽

10.1177/1729881421998585 ◽

2021 ◽

Vol 18 (2) ◽

pp. 172988142199858

Author(s):

Gianpaolo Gulletta ◽

Eliana Costa e Silva ◽

Wolfram Erlhagen ◽

Ruud Meulenbroek ◽

Maria Fernanda Pires Costa ◽

...

Keyword(s):

Motor Control ◽

Upper Limb ◽

Planning Process ◽

Computational Cost ◽

Humanoid Robots ◽

Efficient Manner ◽

Daily Lives ◽

Human Motor Control ◽

Planning Algorithm ◽

Human Motor

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.

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Adiabatic invariants drive rhythmic human motion in variable gravity

10.1101/674143 ◽

2019 ◽

Author(s):

N. Boulanger ◽

F. Buisseret ◽

V. Dehouck ◽

F. Dierick ◽

O. White

Keyword(s):

Motor Control ◽

Human Motion ◽

Adiabatic Invariants ◽

Human Motor Control ◽

Changing Environments ◽

Human Movements ◽

Human Motor ◽

Variable Gravity ◽

Points Of View ◽

Motor Strategies

AbstractNatural human movements are stereotyped. They minimise cost functions that include energy, a natural candidate from mechanical and physiological points of view. In time-changing environments, however, motor strategies are modified since energy is no longer conserved. Adiabatic invariants are relevant observables in such cases, although they have not been investigated in human motor control so far. We fill this gap and show that the theory of adiabatic invariants explains how humans move when gravity varies.

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Toward optimal mapping of human dual-arm motion to humanoid motion for tasks involving contact with the environment

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418757377 ◽

2018 ◽

Vol 15 (1) ◽

pp. 172988141875737 ◽

Cited By ~ 1

Author(s):

Marija Tomić ◽

Kosta Jovanović ◽

Christine Chevallereau ◽

Veljko Potkonjak ◽

Aleksandar Rodić

Keyword(s):

Humanoid Robots ◽

Joint Space ◽

Human Motion ◽

Inverse Optimization ◽

Optimal Combination ◽

Optimization Approach ◽

Control Approach ◽

Criterion Functions ◽

Dual Arm ◽

Weight Coefficients

In this article, we explore human motion skills in the dual-arm manipulation tasks that include contact with equipment with the final aim to generate human-like humanoid motion. Human motion is analyzed using the optimization approaches starting with the assumption that human motion is optimal. A combination of commonly used optimization criteria in the joint space with the weight coefficients is considered: minimization of kinetic energy, minimization of joint velocities, minimization of the distance between the current and ergonomic positions, and maximization of manipulability. The contribution of each criterion for seven different dual-arm manipulation tasks to provide the most accurate imitation of the human motion is given via suggested inverse optimization approach calculating values of weight coefficients. The effects on actors’ body characteristics and the characteristics of the environment (involved equipment) on the choice of criterion functions are additionally analyzed. The optimal combination of weight coefficients calculated by the inverse optimization approach is used in our inverse kinematics algorithm to transfer human motion skills to the motion of the humanoid robots. The results show that the optimal combination of weight coefficients is able to generate human-like humanoid motions rather than individual one of the considered criterion functions. The recorded human motion and the motion of the humanoid robot ROMEO, obtained with the strategy used by human and defined by our inverse optimal control approach, for the tasks “opening/closing a drawer” are assessed visually and quantitatively.

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Synthesizing object receiving motions of humanoid robots with human motion database

2013 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2013.6630788 ◽

2013 ◽

Cited By ~ 16

Author(s):

Katsu Yamane ◽

Marcel Revfi ◽

Tamim Asfour

Keyword(s):

Humanoid Robots ◽

Human Motion

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Force Anticipation and Its Potential Implications on Feedforward and Feedback Human Motor Control

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720819900842 ◽

2020 ◽

pp. 001872081990084

Author(s):

Hideyuki Kimpara ◽

Kenechukwu C. Mbanisi ◽

Zhi Li ◽

Karen L. Troy ◽

Danil Prokhorov ◽

...

Keyword(s):

Motor Control ◽

Preceding Trial ◽

Human Motion ◽

Intelligent Vehicles ◽

Human Motor Control ◽

Human Motor ◽

Resistive Forces ◽

Muscle Groups ◽

Assistive Systems ◽

The Masses

Objective To investigate the effects of human force anticipation, we conducted an experimental load-pushing task with diverse combinations of informed and actual loading weights. Background Human motor control tends to rely upon the anticipated workload to plan the force to exert, particularly in fast tasks such as pushing objects in less than 1 s. The motion and force responses in such tasks may depend on the anticipated resistive forces, based on a learning process. Method Pushing performances of 135 trials were obtained from 9 participants. We varied the workload by changing the masses from 0.2 to 5 kg. To influence anticipation, participants were shown a display of the workload that was either correct or incorrect. We collected the motion and force data, as well as electromyography (EMG) signals from the actively used muscle groups. Results Overanticipation produced overshoot performances in more than 80% of trials. Lighter actual workloads were also associated with overshoot. Pushing behaviors with heavier workloads could be classified into feedforward-dominant and feedback-dominant responses based on the timing of force, motion, and EMG responses. In addition, we found that the preceding trial condition affected the performance of the subsequent trial. Conclusion Our results show that the first peak of the pushing force increases consistently with anticipatory workload. Application This study improves our understanding of human motion control and can be applied to situations such as simulating interactions between drivers and assistive systems in intelligent vehicles.

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