Different Mechanisms Involved in Adaptation to Stable and Unstable Dynamics

2003 ◽  
Vol 90 (5) ◽  
pp. 3255-3269 ◽  
Author(s):  
Rieko Osu ◽  
Etienne Burdet ◽  
David W. Franklin ◽  
Theodore E. Milner ◽  
Mitsuo Kawato
Keyword(s):  
Force Field ◽  
Inverse Dynamics ◽  
Impedance Control ◽  
Early Stage ◽  
Control Mechanisms ◽  
Dynamics Model ◽  
Trajectory Error ◽  
Feedback Error Learning ◽  
Model Formation ◽  
Unstable Dynamics

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a “velocity-dependent force field” (VF), which interacted with the arm in a stable manner, and learning in a “divergent force field” (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

scholarly journals Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model

2003 ◽  
Vol 90 (5) ◽  
pp. 3270-3282 ◽  
Author(s):  
David W. Franklin ◽  
Rieko Osu ◽  
Etienne Burdet ◽  
Mitsuo Kawato ◽  
Theodore E. Milner
Keyword(s):  
Force Field ◽  
Time Course ◽  
Inverse Dynamics ◽  
Force Fields ◽  
Impedance Control ◽  
Joint Torque ◽  
Initial Increase ◽  
Dynamics Model ◽  
Hand Path ◽  
Additional Stability

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Mechanical Impedance Control of Cooperative Robot During Object Manipulation Based on External Force Estimation Using Recurrent Neural Network

2020 ◽  
Vol 08 (03) ◽  
pp. 239-251
Author(s):  
Misaki Hanafusa ◽  
Jun Ishikawa
Keyword(s):  
Neural Network ◽  
External Force ◽  
Recurrent Neural Network ◽  
Inverse Dynamics ◽  
Impedance Control ◽  
Mechanical Impedance ◽  
Object Manipulation ◽  
Force Estimation ◽  
Dynamics Model ◽  
Compliant Motion

This paper proposes a compliant motion control for human-cooperative robots to absorb collision force when persons accidentally touch the robots even while the robot is manipulating an object. In the proposed method, an external force estimator, which can distinguish the net external force from the object manipulation force, is realized using an inverse dynamics model acquired by a recurrent neural network (RNN). By implementing a mechanical impedance control to the estimated external force, the robot can quickly and precisely carry the object keeping the mechanical impedance control functioned and can generate a compliant motion to the net external force only when the person touches it during manipulation. Since the proposed method estimates the external force from the generalized force based on the learned inverse dynamics, it is not necessary to install any sensors on the manipulated object to measure the external force. This allows the robot to detect the collision even when the person touches anywhere on the manipulated object. The RNN inverse dynamics model is evaluated by the leave-one-out cross-validation and it was found that it works well for unknown trajectories excluded from the learning process. Although the details were omitted due to the limitation of the page length, similar to the simulations, the RNN inverse dynamics model was evaluated using unknown trajectories in the six degree-of-freedom experiments, and it has been verified that it functions properly even for the unknown trajectories. Finally, the validity of the proposed method has been confirmed by experiments in which a person touches a robot while it is manipulating an object with six degrees of freedom.

scholarly journals Feedforward compensation for novel dynamics depends on force field orientation but is similar for the left and right arms

2016 ◽  
Vol 116 (5) ◽  
pp. 2260-2271 ◽  
Author(s):  
Eva-Maria Reuter ◽  
Ross Cunnington ◽  
Jason B. Mattingley ◽  
Stephan Riek ◽  
Timothy J. Carroll
Keyword(s):  
Force Field ◽  
Hybrid Control ◽  
Impedance Control ◽  
Dynamic Environment ◽  
Field Trials ◽  
Control Mechanisms ◽  
Functional Requirements ◽  
Field Orientation ◽  
Opposite Limb ◽  
Kinematic Errors

There are well-documented differences in the way that people typically perform identical motor tasks with their dominant and the nondominant arms. According to Yadav and Sainburg's ( Neuroscience 196: 153–167, 2011) hybrid-control model, this is because the two arms rely to different degrees on impedance control versus predictive control processes. Here, we assessed whether differences in limb control mechanisms influence the rate of feedforward compensation to a novel dynamic environment. Seventy-five healthy, right-handed participants, divided into four subsamples depending on the arm (left, right) and direction of the force field (ipsilateral, contralateral), reached to central targets in velocity-dependent curl force fields. We assessed the rate at which participants developed predictive compensation for the force field using intermittent error-clamp trials and assessed both kinematic errors and initial aiming angles in the field trials. Participants who were exposed to fields that pushed the limb toward ipsilateral space reduced kinematic errors more slowly, built up less predictive field compensation, and relied more on strategic reaiming than those exposed to contralateral fields. However, there were no significant differences in predictive field compensation or kinematic errors between limbs, suggesting that participants using either the left or the right arm could adapt equally well to novel dynamics. It therefore appears that the distinct preferences in control mechanisms typically observed for the dominant and nondominant arms reflect a default mode that is based on habitual functional requirements rather than an absolute limit in capacity to access the controller specialized for the opposite limb.

Impedance Control Balances Stability With Metabolically Costly Muscle Activation

2004 ◽  
Vol 92 (5) ◽  
pp. 3097-3105 ◽  
Author(s):  
David W. Franklin ◽  
Udell So ◽  
Mitsuo Kawato ◽  
Theodore E. Milner
Keyword(s):  
Force Field ◽  
Muscle Activation ◽  
Force Fields ◽  
Impedance Control ◽  
Reaching Movements ◽  
The Novel ◽  
Minimum Level ◽  
Stable Direction ◽  
Endpoint Stiffness ◽  
Unstable Dynamics

Humans are able to stabilize their movements in environments with unstable dynamics by selectively modifying arm impedance independently of force and torque. We further investigated adaptation to unstable dynamics to determine whether the CNS maintains a constant overall level of stability as the instability of the environmental dynamics is varied. Subjects performed reaching movements in unstable force fields of varying strength, generated by a robotic manipulator. Although the force fields disrupted the initial movements, subjects were able to adapt to the novel dynamics and learned to produce straight trajectories. After adaptation, the endpoint stiffness of the arm was measured at the midpoint of the movement. The stiffness had been selectively modified in the direction of the instability. The stiffness in the stable direction was relatively unchanged from that measured during movements in a null force field prior to exposure to the unstable force field. This impedance modification was achieved without changes in force and torque. The overall stiffness of the arm and environment in the direction of instability was adapted to the force field strength such that it remained equivalent to that of the null force field. This suggests that the CNS attempts both to maintain a minimum level of stability and minimize energy expenditure.

Estimation of Muscle Forces About the Ankle During Gait in Healthy and Neurologically Impaired Subjects

2011 ◽  
pp. 320-347
Author(s):  
Daniel N. Bassett ◽  
Joseph D. Gardinier ◽  
Kurt T. Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Muscle Activation ◽  
Inverse Dynamics ◽  
Biomechanical Model ◽  
Forward Dynamics ◽  
Muscle Forces ◽  
Dynamics Model ◽  
Joint Moments ◽  
Neurologically Impaired ◽  
Model Predictions ◽  
Contraction Dynamics

This chapter describes a biomechanical model of the forces about the ankle joint applicable to both unimpaired and neurologically impaired subjects. EMGs and joint kinematics are used as inputs and muscle forces are the outputs. A hybrid modeling approach that uses both forward and inverse dynamics is employed and physiological parameters for the model are tuned for each subject using optimization procedures. The forward dynamics part of the model takes muscle activation and uses Hill-type models of muscle contraction dynamics to estimate muscle forces and the corresponding joint moments. Inverse dynamics is used to calibrate the forward dynamics model predictions of joint moments. In this chapter we will describe how to implement an EMG-driven hybrid forward and inverse dynamics model of the ankle that can be used in healthy and neurologically impaired people.

Modeling and Workplace Analysis of 3T Cable-Driven Parallel Manipulator

2013 ◽  
Vol 415 ◽  
pp. 519-523
Author(s):  
Li Wen Guan ◽  
Cheng Long Mu ◽  
Yu Jian Hu
Keyword(s):  
Numerical Simulation ◽  
Dynamic Modeling ◽  
Parallel Manipulator ◽  
Inverse Dynamics ◽  
Comprehensive Analysis ◽  
Dynamics Model ◽  
Set Up

This paper analyzes the dynamic modeling and workplace of the 3T cable-driven parallel manipulator. The mechanism utilizes four cables to driven. Firstly, the kinematics equations and the inverse dynamics model were set up for the analysis. And then, pseudo-drag is a serious problem in cable-driven parallel manipulator, this paper give a method to find the workplace under the condition of pseudo-drag. Finally, through numerical simulation, the workplace of the mechanism satisfying the demand is presented by comprehensive analysis.

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