Neural manifolds for the control of movement

JA Gallego, MG Perich, LE Miller, SA Solla - Neuron, 2017‏ - cell.com
The analysis of neural dynamics in several brain cortices has consistently uncovered low-
dimensional manifolds that capture a significant fraction of neural variability. These neural …

Improving performance of robots using human-inspired approaches: a survey

H Qiao, S Zhong, Z Chen, H Wang - Science China Information Sciences, 2022‏ - Springer
Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …

Beyond simple laboratory studies: Develo** sophisticated models to study rich behavior

A Maselli, J Gordon, M Eluchans, GL Lancia… - Physics of Life …, 2023‏ - Elsevier
Psychology and neuroscience are concerned with the study of behavior, of internal cognitive
processes, and their neural foundations. However, most laboratory studies use constrained …

[HTML][HTML] Muscle synergies in joystick manipulation

L Cai, S Yan, C Ouyang, T Zhang, J Zhu… - Frontiers in …, 2023‏ - frontiersin.org
Extracting muscle synergies from surface electromyographic signals (sEMGs) during
exercises has been widely applied to evaluate motor control strategies. This study explores …

Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement

JL Hicks, TK Uchida, A Seth… - Journal of …, 2015‏ - asmedigitalcollection.asme.org
Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables
researchers and clinicians to study the complex dynamics underlying human and animal …

Muscle coactivation: definitions, mechanisms, and functions

ML Latash - Journal of neurophysiology, 2018‏ - journals.physiology.org
The phenomenon of agonist-antagonist muscle coactivation is discussed with respect to its
consequences for movement mechanics (such as increasing joint apparent stiffness …

One policy to control them all: Shared modular policies for agent-agnostic control

W Huang, I Mordatch, D Pathak - … Conference on Machine …, 2020‏ - proceedings.mlr.press
Reinforcement learning is typically concerned with learning control policies tailored to a
particular agent. We investigate whether there exists a single global policy that can …

Human-in-the-loop optimization of wearable robotic devices to improve human–robot interaction: A systematic review

MA Diaz, M Voß, A Dillen, B Tassignon… - IEEE Transactions …, 2022‏ - ieeexplore.ieee.org
This article presents a systematic review on wearable robotic devices that use human-in-the-
loop optimization (HILO) strategies to improve human–robot interaction. A total of 46 HILO …

Internal models in biological control

D McNamee, DM Wolpert - Annual review of control, robotics …, 2019‏ - annualreviews.org
Rationality principles such as optimal feedback control and Bayesian inference underpin a
probabilistic framework that has accounted for a range of empirical phenomena in biological …

Neuromechanical principles underlying movement modularity and their implications for rehabilitation

LH Ting, HJ Chiel, RD Trumbower, JL Allen, JL McKay… - Neuron, 2015‏ - cell.com
Neuromechanical principles define the properties and problems that shape neural solutions
for movement. Although the theoretical and experimental evidence is debated, we present …