Humanoïde

Résumé

Le terme humanoïde signifie . Il évoque la bipédie, la présence de deux bras et d'une tête. Il s'agit donc uniquement d'un critère phénotypique et plus précisément morphologique. Terminologie Le terme humanoïde est utilisé dans la science-fiction et la fantasy, ou dans la robotique. On le trouve aussi dans le domaine de la cryptozoologie. Ce mot ne doit pas être confondu avec « hominoïde », « hominidé », « homininé », « hominine » ou « hominien », et tous termes taxonomiques de définition plus rigoureuse. Technologie Un robot humanoïde est un robot dont l'apparence générale rappelle celle d'un corps humain. Généralement, les robots humanoïdes ont un torse avec une tête, deux bras et deux jambes, bien que certains modèles ne représentent qu'une partie du corps, par exemple à partir de la taille. Certains robots humanoïdes peuvent avoir un « visage », avec des « yeux » et une « bouche ». Culture et littérature Les reptiles humanoïdes, êtres mythiq

Source officielle

https://fr.wikipedia.org/wiki/Humano%C3%AFde

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A humanoid robot is a robot resembling the human body in shape. The design may be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as t

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This paper deals with whole-body motion planning and dynamic control for humanoid from two aspects: locomotion including manipulation and reaching. In the first part, we address a problem of simultaneous locomotion and manipulation planning that combines a geometric and kinematic motion planner with a dynamic humanoid motion generator. The second part deals with whole-body reaching tasks by using a generalized inverse kinematics (IK) method to fully exploit the high redundancy of the humanoid robot. Through experiments using humanoid platform HRP-2 No. 14 installed at LAAS-CNRS, we first verify the validity of each method. An integrated experiment is then presented that unifies the both results via visual perception to execute an object-fetching task. Copyright (C) 2009 John Wiley & Sons, Ltd.

2009

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Adaptive Task-Space Force Control for Humanoid-to-Human Assistance

Anastasia Bolotnikova

We envision a humanoid robot to serve as a source of additional motion-support forces in assistance for frail persons. In this context, we present a control strategy for a humanoid to adaptively regulate its assistive force contribution. First, we identify a human model torque control for optimal execution of a priori known motion task from sample recordings of this task performed by a healthy individual. We utilize the identified model in the proposed position discrepancy based observer of the human torque contribution, the unknown and unmeasurable variable. We propose an experience-based human contribution model learning strategy that allows to improve the human contribution estimate from trial-to-trial. The target assistive torque contribution is then calculated as the difference between the optimal torque required for the motion task and the estimated human contribution. The target assistive torque is integrated into a multi-robot quadratic programming task-space controller to compute the desired interaction force required for the robot to supply the necessary assistive torque for the human model. We use the non-optimal recordings of the human motion task to emulate human frailty and apply our adaptive force control strategy to demonstrate the results of a humanoid successfully assisting the simulated human model to restore the optimal motion task performance.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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Adaptive sensorimotor peripersonal space representation and motor learning for a humanoid robot

Micha Hersch

This thesis presents possible computational mechanisms by which a humanoid robot can develop a coherent representation of the space within its reach (its peripersonal space), and use it to control its movements. Those mechanisms are inspired by current theories of peripersonal space representation and motor control in humans, targeting a cross-fertilization between robotics on one side, and cognitive science on the other side. This research addresses the issue of adaptivity the sensorimotor level, at the control level and at the level of simple task learning. First, this work considers the concept of body schema and suggests a computational translation of this concept, appropriate for controlling a humanoid robot. This model of the body schema is adaptive and evolves as a result of the robot sensory experience. It suggests new avenues for understanding various psychophysical and neuropsychological phenomenons of human peripersonal space representation such as adaptation to distorted vision and tool use, fake limbs experiments, body-part centered receptive fields, and multimodal neurons. Second, it is shown how the motor modality can be added to the body schema. The suggested controller is inspired by the dynamical system theory of motor control and allows the robot to simultaneously and robustly control its limbs in joint angles space and in end-effector location space. This amounts to controlling the robot in both proprioceptive and visual modalities. This multimodal control can benefit from the advantages offered by each modality and is better than traditional robotic controllers in several respects. It offers a simple and elegant solution to the singularity and joint limit avoidance problems and can be seen as a generalization of the Damped Least Square approach to robot control. The controller exhibits several properties of human reaching movements, such as quasi-straight hand paths and bell-shaped velocity profiles and non-equifinality. In a third step, the motor modalities is endowed with a statistical learning mechanism, based on Gaussian Mixture Models, that enables the humanoid to learn motor primitives from demonstrations. The robot is thus able to learn simple manipulation tasks and generalize them to various context, in a way that is robust to perturbations occurring during task execution. In addition to simulation results, the whole model has been implemented and validated on two humanoid robots, the Hoap3 and the iCub, enabling them to learn their arm and head geometries, perform reaching movements, adapt to unknown tools, and visual distortions, and learn simple manipulation tasks in a smooth, robust and adaptive way. Finally, this work hints at possible computational interpretations of the concepts of body schema, motor perception and motor primitives.

EPFL2009

Chargement

Adaptive sensorimotor peripersonal space representation and motor learning for a humanoid robot

Micha Hersch

EPFL2009