Learning to push and learning to move: The adaptive control of contact forces

To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in \u201ccompatible pairs\u201d connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

Adaptation to Delayed Force Perturbations in Reaching Movements

Adaptation to deterministic force perturbations during reaching movements was extensively studied in the last few decades. Here, we use this methodology to explore the ability of the brain to adapt to a delayed velocity-dependent force field. Two groups of subjects preformed a standard reaching experiment under a velocity dependent force field. The force was either immediately proportional to the current velocity (Control) or lagged it by 50 ms (Test). The results demonstrate clear adaptation to the delayed force perturbations. Deviations from a straight line during catch trials were shifted in time compared to post-adaptation to a non-delayed velocity dependent field (Control), indicating expectation to the delayed force field. Adaptation to force fields is considered to be a process in which the motor system predicts the forces to be expected based on the state that a limb will assume in response to motor commands. This study demonstrates for the first time that the temporal window of this prediction needs not to be fixed. This is relevant to the ability of the adaptive mechanisms to compensate for variability in the transmission of information across the sensory-motor system

Perception of delayed stiffness

Advanced technology has recently provided truly immersive virtual environments with teleoperated robotic devices. In order to control movements from a distance, the human sensorimotor system has to overcome the effects of delay. Currently, little is known about the mechanisms that underlie haptic estimation in delayed environments. The aim of this research is to explore the effect of a delay on perception of surfaces stiffness. We used a forced choice paradigm in which subjects were asked to identify the stiffer of two virtual spring-like surfaces based on manipulation without visual feedback. Virtual surfaces were obtained by generating an elastic force proportional to the penetration of the handle of a manipulandum inside a virtual boundary. The elastic force was either an instantaneous function of the displacement, delayed at 30 or 60 milliseconds after the displacement or led the displacement (by means of Kalman predictor) by 50 milliseconds. We assume that for estimating stiffness, the brain relates the experienced interaction force

Data from: Energy exchanges at contact events guide sensorimotor integration across intermodal delays

The brain must consider the arm's inertia to predict the arm's movements elicited by commands impressed upon the muscles. Here, we present evidence suggesting that the integration of sensory information leading to the representation of the arm's inertia does not take place continuously in time but only at discrete transient events, in which kinetic energy is exchanged between the arm and the environment. We used a visuomotor delay to induce cross-modal variations in state feedback and uncovered that the difference between visual and proprioceptive velocity estimations at isolated collision events was compensated by a change in the representation of arm inertia. The compensation maintained an invariant estimate across modalities of the expected energy exchange with the environment. This invariance captures different types of dysmetria observed across individuals following prolonged exposure to a fixed intermodal temporal perturbation and provides a new interpretation for cerebellar ataxia

Adaptation to delayed force perturbations.

<p>(a,b) Maximal Perpendicular Distance in training sessions, not including catch and after-catch trials; the slant line is an exponential fit to the data. (c,d) The mean PD of the last catch trials (CT) of a typical subject in Test (blue) and Control (red) during training sessions (sessions 3–5). Data presented is PD normalized by movement length for short (SM) and long (LM) movements. Error bars show a single standard deviation of the mean. Note that the graphs are truncated at t = 300ms, as the post-correction part of the movement is not relevant for analysis of deviation start point. (e,f) Typical velocity profiles for Test and Control groups during pre training and catch trials. Color code is the same as above.</p

System setup and target locations.

<p>a) System Setup. The red cross is the target and the red dot is the cursor representing the hand position. b) Targets setup, the short and long arrows (dashed and sloid) show the length of the short (10 cm) and long motions (15 cm), accordingly. c) The arrows point to a typical 4 target sequence starting at target 1. Once reaching a target a limited selection was avilable for the next one. For instance, from target number 3 only target 2 or 4 would have been valid (in the currnet case, target 4 was reached).</p

Single subject reaching trajectories.

<p>Reaching of typical subjects from both Test group (top row) and Control group (bottom row). From left to right the pre-exposure, baseline and catch trials are shown. It is evident that the test group corrects later throughout the motion than the control group. Shown is the average of all movements on each block for the pre-exposure and catch trials and the average of the last 8 movements of the baseline.</p