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.

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.

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.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

2015 ◽  
Vol 113 (2) ◽  
pp. 487-508 ◽  
Author(s):  
Touria Addou ◽  
Nedialko I. Krouchev ◽  
John F. Kalaska
Keyword(s):  
Motor Cortex ◽  
Force Field ◽  
Time Course ◽  
Primary Motor Cortex ◽  
Force Fields ◽  
Elastic Force ◽  
Pseudorandom Sequence ◽  
Sample Population ◽  
Dynamic Force ◽  
Flexion Extension

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

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.

Hydration Free Energies of Organic Molecules in the FreeSolv Database Calculated with Polarized Atom In Molecules Atomic Charges and the GAFF Force Field.

2018 ◽  
Author(s):  
Maximiliano Riquelme ◽  
Alejandro Lara ◽  
David L. Mobley ◽  
Toon Vestraelen ◽  
Adelio R Matamala ◽  
...  
Keyword(s):  
Force Field ◽  
Functional Groups ◽  
Electrostatic Interactions ◽  
Organic Molecules ◽  
Force Fields ◽  
Chemical Elements ◽  
Atomic Charges ◽  
Free Energies ◽  
Molecular Systems ◽  
Solvent Model

<div>Computer simulations of bio-molecular systems often use force fields, which are combinations of simple empirical atom-based functions to describe the molecular interactions. Even though polarizable force fields give a more detailed description of intermolecular interactions, nonpolarizable force fields, developed several decades ago, are often still preferred because of their reduced computation cost. Electrostatic interactions play a major role in bio-molecular systems and are therein described by atomic point charges.</div><div>In this work, we address the performance of different atomic charges to reproduce experimental hydration free energies in the FreeSolv database in combination with the GAFF force field. Atomic charges were calculated by two atoms-in-molecules approaches, Hirshfeld-I and Minimal Basis Iterative Stockholder (MBIS). To account for polarization effects, the charges were derived from the solute's electron density computed with an implicit solvent model and the energy required to polarize the solute was added to the free energy cycle. The calculated hydration free energies were analyzed with an error model, revealing systematic errors associated with specific functional groups or chemical elements. The best agreement with the experimental data is observed for the MBIS atomic charge method, including the solvent polarization, with a root mean square error of 2.0 kcal mol<sup>-1</sup> for the 613 organic molecules studied. The largest deviation was observed for phosphor-containing molecules and the molecules with amide, ester and amine functional groups.</div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

A Transferable, Polarisable Force Field for Ionic Liquids

2019 ◽  
Author(s):  
Kateryna Goloviznina ◽  
José N. Canongia Lopes ◽  
Margarida Costa Gomes ◽  
Agilio Padua
Keyword(s):  
Ionic Liquids ◽  
Force Field ◽  
Force Fields ◽  
Dipole Moments ◽  
Van Der Waals Interactions ◽  
First Principles Calculations ◽  
Ion Diffusion ◽  
Fixed Integer ◽  
Molecular Compounds ◽  
Induced Dipoles

A general, transferable polarisable force field for molecular simulation of ionic liquids and their mixtures with molecular compounds is developed. This polarisable model is derived from the widely used CL\&P fixed-charge force field that describes most families of ionic liquids, in a form compatible with OPLS-AA, one of the major force fields for organic compounds. Models for ionic liquids with fixed, integer ionic charges lead to pathologically slow dynamics, a problem that is corrected when polarisation effects are included explicitly. In the model proposed here, Drude induced dipoles are used with parameters determined from atomic polarisabilities. The CL\&P force field is modified upon inclusion of the Drude dipoles, to avoid double-counting of polarisation effects. This modification is based on first-principles calculations of the dispersion and induction contributions to the van der Waals interactions, using symmetry-adapted perturbation theory (SAPT) for a set of dimers composed of positive, negative and neutral fragments representative of a wide variety of ionic liquids. The fragment approach provides transferability, allowing the representation of a multitude of cation and anion families, including different functional groups, without need to re-parametrise. Because SAPT calculations are expensive an alternative predictive scheme was devised, requiring only molecular properties with a clear physical meaning, namely dipole moments and atomic polarisabilities. The new polarisable force field, CL\&Pol, describes a broad set set of ionic liquids and their mixtures with molecular compounds, and is validated by comparisons with experimental data on density, ion diffusion coefficients and viscosity. The approaches proposed here can also be applied to the conversion of other fixed-charged force fields into polarisable versions.<br>

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