Analogous adaptations in speed, impulse and endpoint stiffness when learning a real and virtual insertion task with haptic feedback

Humans have the ability to use a diverse range of handheld tools. Owing to its versatility, a virtual environment with haptic feedback of the force is ideally suited to investigating motor learning during tool use. However, few simulators exist to recreate the dynamic interactions during real tool use, and no study has compared the correlates of motor learning between a real and virtual tooling task. To this end, we compared two groups of participants who either learned to insert a real or virtual tool into a fixture. The trial duration, the movement speed, the force impulse after insertion and the endpoint stiffness magnitude decreased as a function of trials, but they changed at comparable rates in both environments. A ballistic insertion strategy observed in both environments suggests some interdependence when controlling motion and controlling interaction, contradicting a prominent theory of these two control modalities being independent of one another. Our results suggest that the brain learns real and virtual insertion in a comparable manner, thereby supporting the use of a virtual tooling task with haptic feedback to investigate motor learning during tool use.

Optimal Realization of Endpoint Stiffness in Static Human Arm Postures

The endpoint stiffness, i.e., stiffness displayed by the wrist amid perturbations to the arm, has been used to assess the mechanical stability of the arm posture. The aim of this study is to develop an algorithm to optimally realize a desired end-point stiffness by minimizing muscle forces. The neuro-muscular behavior of the human arm during posture maintenance tasks is approximated by a two-link eight-muscle arm model. The model parameters reflect physiological data taken from published literature. The endpoint stiffness is shown to be a linear function of muscle activations. It is shown that the problem to minimize muscle activations while satisfying torque constraint at the joints can be solved by using non-negative least-squares method. Alternatively, linear programming can be used for this purpose. The biomechanical model is used to demonstrate how endpoint stiffness of desired magnitude, orientation, and eccentricity can be synthesized by activating arm muscles with minimal energy expenditure. Our simulation results suggest that bi-articular muscles play a major role in developing the desired endpoint stiffness. The model can be scaled up to three-dimensions by adding muscle groups.

An Intuitive Formulation of the Human Arm Active Endpoint Stiffness

In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition Kc=VDVT, where V is an orthonormal matrix whose columns are the normalized eigenvectors of Kc, and D is a diagonal matrix whose entries are the eigenvalues of Kc. In this formulation, we propose to construct V and D directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human–robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement.

Design of a parallel compliance device with variable stiffness

This paper presents a parallel compliance device with variable translational stiffness properties. The variation of endpoint stiffness depends on the change of the spring stiffness in each limb. A synthesis algorithm for realizing the desired force compliance performance is built. Based on the proposed algorithm, a group of optimal spring stiffness can be derived. For the implementation of this device, an electromagnetic linear spring with current-controlled stiffness is developed. After testing the mechanical characteristics of the electromagnetic spring, a prototype of the parallel compliance device is built. The endpoint stiffness under different combinations of spring stiffness values is exhibited in the form of stiffness ellipsoids. A case is studied and verifies the ability of the presented compliance device to realize the desired endpoint stiffness. As the stiffness adjustment range of electromagnetic spring is limited, the bound of physically realizable stiffness of the presented compliance device is also discussed.

The Concurrent Control of Motion and Contact Force in the Presence of Predictable Disturbances

The simultaneous control of force and motion is important in everyday activities when humans interact with objects. While many studies have analyzed the control of movement within a perturbing force field, few have investigated its dual aspects of controlling a contact force in nonisometric conditions. The mechanism by which the central nervous system controls forces during movements is still unclear, and it can be elucidated by estimating the mechanical properties of the arm during tasks with concurrent motion and contact force goals. We investigate how arm mechanics change when a force control task is accomplished during low-frequency positional perturbations of the arm. Contrary to many force regulation algorithms implemented in robotics, where contact impedance is decreased to reduce force fluctuations in response to position disturbances, we observed a steady increase of arm endpoint stiffness as the task progressed. Based on this evidence, we propose a theoretical framework suggesting that an internal model of the perturbing trajectory is formed. We observed that force regulation in the presence of predictable positional disturbances is implemented using a position control strategy together with the modulation of the endpoint stiffness magnitude, where the direction of the endpoint stiffness ellipse's major axis is oriented toward the desired force.

Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation

In this paper, a reduced-complexity model of the human arm endpoint stiffness is introduced and experimentally evaluated for the teleimpedance control of a compliant robotic arm. The modeling of the human arm endpoint stiffness behavior is inspired by human motor control principles on the predominant use of the arm configuration in directional adjustments of the endpoint stiffness profile, and the synergistic effect of muscular activations, which contributes to a coordinated modification of the task stiffness in all Cartesian directions. Calibration and identification of the model parameters are carried out experimentally, using perturbation-based arm endpoint stiffness measurements in different arm configurations and cocontraction levels of the chosen muscles. Consequently, the real-time model is used for the remote control of a compliant robotic arm while executing a drilling task, a representative example of tool use in environments with constraints and dynamic uncertainties. The results of this study illustrate that the proposed model enables the master to execute the remote task by modulation of the directions of the major axes of the endpoint stiffness ellipsoid and its volume using natural arm configurations and the cocontraction of the involved muscles, respectively.