A Theory of Impedance Control based on Internal Model Uncertainty

Efficient human motor control is characterised by an extensive use of joint impedance modulation, which to a large extent is achieved by co-contracting antagonistic muscle pairs in a way that is beneficial to the specific task. Studies in single and multi joint limb reaching movements revealed that joint impedance is increased with faster movements [1] as well as with higher positional accuracy demands [2]. A large body of experimental work has investigated the motor learning processes in tasks with changing dynamics conditions (e.g., [3]) and it has been shown that subjects generously make use of impedance control to counteract destabilising external force fields (FF). In the early stage of dynamics learning humans tend to increase co-contraction. As learning progresses in consecutive reaching trials, a reduction in co-contraction with a parallel reduction of the reaching errors made can be observed. While there is much experimental evidence available for the use of impedance control in the CNS, no generally-valid computational model of impedance control derived from first principles have been proposed so far. Many of the proposed computational models have either focused on the biomechanical aspects of impedance control [4] or have proposed simple low level mechanisms to try to account for observed human co-activation patterns [3]. However these models are of a rather descriptive nature and do not provide us with a general and principled theory of impedance control in the nervous system

A versatile biomimetic controller for contact tooling and haptic exploration

International audienceThis article presents a versatile controller that enables various contact tooling tasks with minimal prior knowledge of the tooled surface. The controller is derived from results of neuroscience studies that investigated the neural mechanisms utilized by humans to control and learn complex interactions with the environment. We demonstrate here the versatility of this controller in simulations of cutting, drilling and surface exploration tasks, which would normally require different control paradigms. We also present results on the exploration of an unknown surface with a 7-DOF manipulator, where the robot builds a 3D surface map of the surface profile and texture while applying constant force during motion. Our controller provides a unified control framework encompassing behaviors expected from the different specialized control paradigms like position control, force control and impedance control

A Computational Model of Limb Impedance Control Based on Principles of Internal Model Uncertainty

Efficient human motor control is characterized by an extensive use of joint impedance modulation, which is achieved by co-contracting antagonistic muscles in a way that is beneficial to the specific task. While there is much experimental evidence available that the nervous system employs such strategies, no generally-valid computational model of impedance control derived from first principles has been proposed so far. Here we develop a new impedance control model for antagonistic limb systems which is based on a minimization of uncertainties in the internal model predictions. In contrast to previously proposed models, our framework predicts a wide range of impedance control patterns, during stationary and adaptive tasks. This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands. The insights from this computational model could be used to interpret existing experimental impedance control data from the viewpoint of optimality or could even govern the design of future experiments based on principles of internal model uncertainty

Identification and Modeling of Sensory Feedback Processing in a Brain System for Voluntary Movement Control

The body shows impressive control capabilities in terms of the speed and the precision with which movements can be carried out under a wide variety of circumstances. The cerebellum and brainstem nuclei, including the cuneate nucleus, are believed to play a crucial role in this control. If these control mechanisms can be unveiled, this could yield important insights in not only medicine and neurophysiology, but could also control theory in general, which could then potentially be applied in a variety of industry-based control applications. In this thesis system identification and modeling of one subsystem is considered: the cuneate nucleus. The aim of this project is to create a quantitative model for control of a network of neurons in this structure and to create a detailed single-cell model of the cuneate neuron. A two-pronged approach is used to study the function of this structure. First a black-box like system identification using Matlab with experimental data as in- and output signals is considered. Then, building on a previously developed Scicos neuron model, a detailed neuron model of one cuneate neuron is developed, incorporating many aspects of recently described cellular neurophysiology. Our findings suggest that the cuneate nucleus acts as filter for its input sensory signal, applying a differentiating and phase-lead effect on the transmitted signal. These are interesting features of a control system, and could help understand how the body can attain such a high degree of precision in its movements

Learning Riemannian Stable Dynamical Systems via Diffeomorphisms

Dexterous and autonomous robots should be capable of executing elaborated dynamical motions skillfully. Learning techniques may be leveraged to build models of such dynamic skills. To accomplish this, the learning model needs to encode a stable vector field that resembles the desired motion dynamics. This is challenging as the robot state does not evolve on a Euclidean space, and therefore the stability guarantees and vector field encoding need to account for the geometry arising from, for example, the orientation representation. To tackle this problem, we propose learning Riemannian stable dynamical systems (RSDS) from demonstrations, allowing us to account for different geometric constraints resulting from the dynamical system state representation. Our approach provides Lyapunov-stability guarantees on Riemannian manifolds that are enforced on the desired motion dynamics via diffeomorphisms built on neural manifold ODEs. We show that our Riemannian approach makes it possible to learn stable dynamical systems displaying complicated vector fields on both illustrative examples and real-world manipulation tasks, where Euclidean approximations fail.Comment: To appear at CoRL 202

Increasing muscle co-contraction speeds up internal model acquisition during dynamic motor learning.

During reaching movements in the presence of novel dynamics, participants initially co-contract their muscles to reduce kinematic errors and improve task performance. As learning proceeds, muscle co-contraction decreases as an accurate internal model develops. The initial co-contraction could affect the learning of the internal model in several ways. By ensuring the limb remains close to the target state, co-contraction could speed up learning. Conversely, by reducing kinematic errors, a key training signal, it could slow down learning. Alternatively, given that the effects of muscle co-contraction on kinematic errors are predictable and could be discounted when assessing the internal model error, it could have no effect on learning. Using a sequence of force pulses, we pretrained two groups to either co-contract (stiff group) or relax (relaxed group) their arm muscles in the presence of dynamic perturbations. A third group (control group) was not pretrained. All groups performed reaching movements in a velocity-dependent curl field. We measured adaptation using channel trials and found greater adaptation in the stiff group during early learning. We also found a positive correlation between muscle co-contraction, as measured by surface electromyography, and adaptation. These results show that muscle co-contraction accelerates the rate of dynamic motor learning

Human movement modifications induced by different levels of transparency of an active upper limb exoskeleton

Active upper limb exoskeletons are a potentially powerful tool for neuromotor rehabilitation. This potential depends on several basic control modes, one of them being transparency. In this control mode, the exoskeleton must follow the human movement without altering it, which theoretically implies null interaction efforts. Reaching high, albeit imperfect, levels of transparency requires both an adequate control method and an in-depth evaluation of the impacts of the exoskeleton on human movement. The present paper introduces such an evaluation for three different “transparent” controllers either based on an identification of the dynamics of the exoskeleton, or on force feedback control or on their combination. Therefore, these controllers are likely to induce clearly different levels of transparency by design. The conducted investigations could allow to better understand how humans adapt to transparent controllers, which are necessarily imperfect. A group of fourteen participants were subjected to these three controllers while performing reaching movements in a parasagittal plane. The subsequent analyses were conducted in terms of interaction efforts, kinematics, electromyographic signals and ergonomic feedback questionnaires. Results showed that, when subjected to less performing transparent controllers, participants strategies tended to induce relatively high interaction efforts, with higher muscle activity, which resulted in a small sensitivity of kinematic metrics. In other words, very different residual interaction efforts do not necessarily induce very different movement kinematics. Such a behavior could be explained by a natural human tendency to expend effort to preserve their preferred kinematics, which should be taken into account in future transparent controllers evaluation

Towards a Biomarker of Motor Adaptation: Integration of Kinematic and Neural Factors

We propose an experimental protocol for the integrated study of motor adaptation during target-based movements. We investigated how motor adaptation affects both cerebral activity and motor performance during the preparation and execution of a pointing task, under different conditions of external perturbation. Electroencephalography (EEG) and movement analysis were simultaneously recorded from 16 healthy subjects enrolled in the study. EEG signal was preprocessed bymeans of independent component analysis and empirical mode decomposition based Hilbert Huang transform, in order to extract event-related synchronization (ERS) and desynchronization (ERD) parameters. Movement analysis provided several kinematic indexes, such as movement durations, average jerk, and inter-quartile-ranges. Significant correlations between score, neural, and kinematic parameters were found. Specifically, the duration of the going phase of movement was found to correlate with synchronization in the beta brain rhythm, in both the planning and executive phases of movement. Inter-quartile ranges and average jerk showed correlations with executive brain parameters and ERS/ERDcueBeta, respectively. Results indicate the presence of links between the primary motor cortex and the farthest ending point of the upper limb. In the present study, we assessed significant relationship between neural and kinematic descriptors of motor adaptation, during a protocol requiring short-term learning, through the modulation of the external perturbations

Sensorimotor Control of 3D Arm Movement and Stability in Post-Stroke Hemiparesis

Deficits of the affected arm in people with post-stroke hemiparesis have been generally associated with decreased strength and increased spasticity. These deficits are varied in proximal (shoulder) and distal (elbow) joints which results in an overall impairment during movement or during stabilization of hand position in space. In this study, reaching of the hemiparetic arm in 3D workspace was characterized by a curved and non-smooth endpoint trajectory and a reduced functional range of motion, compared to the unimpaired arm. Smoother trajectories were observed in the acceleration phase more than the deceleration phase, which was common to both the stroke subjects and the neurologically intact controls. Decreased range of motion of the paretic arm in the proximal joint was associated with shoulder weakness, whereas limited range of motion in the elbow appeared to be due to increased antagonist muscle activation. In a task requiring subjects to stabilize their hand at different positions in space, arm weakness and movement synergy constraints may have contributed to stroke survivors generally decreasing the plane of elevation in order to maintain stable arm postures during movement and then stabilize the hand in space. The degree of decreased plane of elevation was negatively correlated with the Fugl-Meyer score. For a task when fine control movement was required simultaneously with a stable arm posture, stroke subjects demonstrated an inability to grade fine muscle control, resulting in larger range of the plane of elevation movements and larger endpoint error. These findings suggest that shoulder strength training might have important implications to the recovery of movement and ability to stabilize the hemiparetic arm during functional tasks