Etienne Burdet

The central nervous system stabilizes unstable dynamics by learning optimal impedance.

To manipulate objects or to use tools we must compensate for any forces arising from interaction with the physical environment. Recent studies indicate that this compensation is achieved by learning an internal model of the dynamics, that is, a neural representation of the relation between motor command and movement. In these studies interaction with the physical environment was stable, but many common tasks are intrinsically unstable. For example, keeping a screwdriver in the slot of a screw is unstable because excessive force parallel to the slot can cause the screwdriver to slip and because misdirected force can cause loss of contact between the screwdriver and the screw. Stability may be dependent on the control of mechanical impedance in the human arm because mechanical impedance can generate forces which resist destabilizing motion. Here we examined arm movements in an unstable dynamic environment created by a robotic interface. Our results show that humans learn to stabilize unstable dynamics using the skilful and energy-efficient strategy of selective control of impedance geometry.

Variable impedance actuators: A review

Variable Impedance Actuators (VIA) have received increasing attention in recent years as many novel applications involving interactions with an unknown and dynamic environment including humans require actuators with dynamics that are not well-achieved by classical stiff actuators. This paper presents an overview of the different VIAs developed and proposes a classification based on the principles through which the variable stiffness and damping are achieved. The main classes are active impedance by control, inherent compliance and damping actuators, inertial actuators, and combinations of them, which are then further divided into subclasses. This classification allows for designers of new devices to orientate and take inspiration and users of VIAs to be guided in the design and implementation process for their targeted application.

A Brain Controlled Wheelchair to Navigate in Familiar Environments

While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.

Fabrication of 3D chitosan–hydroxyapatite scaffolds using a robotic dispensing system

Abstract A new robotic desktop rapid prototyping (RP) system was designed to fabricate scaffolds for tissue engineering applications. The experimental setup consists of a computer-guided desktop robot and a one-component pneumatic dispenser. The dispensing material (chitosan and chitosan–hydroxyapatite (HA) dissolved in acetic acid) was stored in a 30-ml barrel and forced out through a small Teflon-lined nozzle into a dispensing medium (sodium hydroxide–ethanol in ratio of 7:3). Layer-by-layer, the chitosan was fabricated with a preprogramed lay-down pattern. Neutralization of the chitosan forms a gel-like precipitate, and the hydrostatic pressure in the sodium hydroxide (NaOH) solution keeps the cuboid scaffold in shape. Comparison of the freeze-dried scaffold to the wet one showed linear and volumetric shrinkage of about 31% and 62%, respectively. A good attachment between layers allowed the chitosan matrix to form a fully interconnected channel architecture. Results of in vitro cell culture studies revealed the scaffold biocompatibility. The results of this preliminary study using the rapid prototyping robotic dispensing (RPBOD) system demonstrated its potential in fabricating three-dimensional (3D) scaffolds with regular and reproducible macropore architecture.

Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment

It has been shown that humans are able to selectively control the endpoint impedance of their arms when moving in an unstable environment. However, directional instability was only examined for the case in which the main contribution was from coactivation of biarticular muscles. The goal of this study was to examine whether, in general, the CNS activates the sets of muscles that contribute to selective control of impedance in particular directions. Subjects performed reaching movements in three differently oriented unstable environments generated by a robotic manipulandum. After subjects had learned to make relatively straight reaching movements in the unstable force field, the endpoint stiffness of the limb was measured at the midpoint of the movements. For each force field, the endpoint stiffness increased in a specific direction, whereas there was little change in stiffness in the orthogonal direction. The increase in stiffness was oriented along the direction of instability in the environment, which caused the major axis of the stiffness ellipse to rotate toward the instability in the environment. This study confirms that the CNS is able to control the endpoint impedance of the limbs and selectively adapt it to the environment. Furthermore, it supports the idea that the CNS incorporates an impedance controller that acts to ensure stability, reduce movement variability, and reduce metabolic cost.

CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.

MRI/fMRI-compatible robotic system with force feedback for interaction with human motion

This paper presents a robotic system that is compatible with anatomical magnetic resonance imaging (MRI) as well as with the more sensitive functional MRI (fMRI), and can safely and smoothly interact with human motion during the imaging. The system takes advantage of the electromagnetic shield that encloses the MR room by placing the interfering or sensitive components outside the shield, in the control room. This eliminates the need for extensive compatibility testing before each use. The concept is based on a conventional actuator placed outside the scanner room and a hydrostatic connection to transmit force and motion to an MR-compatible slave placed next to or inside the MR scanner. A force sensor, based on reflected light intensity measurement over optical fibers, measures interaction forces with the human subject. A robotic interface for wrist motion demonstrates the MR compatibility of this concept and the possibility to interact with various dynamic environments during functional imaging. This technology provides a basis for applications such as assistive devices for interventional MRI and haptic interfaces for neuroscience investigations.

Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable dynamics

This study compared the mechanisms of adaptation to stable and unstable dynamics from the perspective of changes in joint mechanics. Subjects were instructed to make point to point movements in force fields generated by a robotic manipulandum which interacted with the arm in either a stable or an unstable manner. After subjects adjusted to the initial disturbing effects of the force fields they were able to produce normal straight movements to the target. In the case of the stable interaction, subjects modified the joint torques in order to appropriately compensate for the force field. No change in joint torque or endpoint force was required or observed in the case of the unstable interaction. After adaptation, the endpoint stiffness of the arm was measured by applying displacements to the hand in eight different directions midway through the movements. This was compared to the stiffness measured similarly during movements in a null force field. After adaptation, the endpoint stiffness under both the stable and unstable dynamics was modified relative to the null field. Adaptation to unstable dynamics was achieved by selective modification of endpoint stiffness in the direction of the instability. To investigate whether the change in endpoint stiffness could be accounted for by change in joint torque or endpoint force, we estimated the change in stiffness on each trial based on the change in joint torque relative to the null field. For stable dynamics the change in endpoint stiffness was accurately predicted. However, for unstable dynamics the change in endpoint stiffness could not be reproduced. In fact, the predicted endpoint stiffness was similar to that in the null force field. Thus, the change in endpoint stiffness seen after adaptation to stable dynamics was directly related to changes in net joint torque necessary to compensate for the dynamics in contrast to adaptation to unstable dynamics, where a selective change in endpoint stiffness occurred without any modification of net joint torque.

Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions

This paper presents a novel human-like learning controller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, the controller compensates for the disturbance in the environment in interaction tasks by adapting feedforward force and impedance. In contrast with conventional learning controllers, the new controller can deal with unstable situations that are typical of tool use and gradually acquire a desired stability margin. Simulations show that this controller is a good model of human motor adaptation. Robotic implementations further demonstrate its capabilities to optimally adapt interaction with dynamic environments and humans in joint torque controlled robots and variable impedance actuators, without requiring interaction force sensing.

Robot-assisted rehabilitation of hand function.

PURPOSE OF REVIEW Initial work on robot-assisted neurorehabilitation for the upper extremity aimed primarily at training, reaching movements with the proximal sections of the upper extremity. However, recent years have seen a surge in devices dedicated to hand function. This review describes the state of the art and the promises of this novel therapeutic approach. RECENT FINDINGS Numerous robotic devices for hand function with various levels of complexity and functionality have been developed over the last 10 years. These devices range from simple mechanisms that support single joint movements to mechanisms with as many as 18 degrees-of-freedom (DOF) that can support multijoint movements at the wrist and fingers. The results from clinical studies carried out with eight out of 30 reported devices indicate that robot-assisted hand rehabilitation reduces motor impairments of the affected hand and the arm, and improves the functional use of the affected hand. SUMMARY The current evidence in support of the robot-assisted hand rehabilitation is preliminary but very promising, and provides a strong rationale for more systematic investigations in the future.