Using pre-recorded human motion and trajectory tracking, we can control the motion of a humanoid robot for free-space, upper body gestures. However, the number of degrees of freedom, range of joint motion, and achievable joint velocities of today’s humanoid robots are far more limited than those of the average human subject. In this paper, we explore a set of techniques for limiting human motion of upper body gestures to that achievable by a Sarcos humanoid robot located at ATR. We assess the quality of the results by comparing the motion of the human actor to that of the robot, both visually and quantitatively.

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There are numerous applications of VR simulation re-quiring the grasping and manipulation of virtual objects. Standard-use haptic interfaces (e.g. Virtuose 6D, PHANToM) allow only a limited level of realism, as grasping is approximated through a metaphor (e.g. pressing a button for grasping an object). Existing hand exoskeletons have also certain drawbacks (e.g. feedback only for finger flexion, limited finger workspace etc.). The work presented in this paper introdu-ces a hand force exoskeleton that allows full finger flexion and extension and applies bi-directional feed-back. It has 3dof at the index finger and 4 at the thumb. The system is actuated by DC motors and cable transmissions are used. It has been designed for use in conjunction with Virtuose 6D, a commercial 6dof haptic arm, in order to allow the simulation of external forces (gravity, contact reaction forces, etc.).

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Although below-knee prostheses have been commercially available for some time, today’s devices are completely passive, and consequently, their mechanical properties remain fixed with walking speed and terrain. To improve the current performance of below-knee prostheses, we study the feasibility of using the amputee’s residual limb EMG signals to control the ankle position of an active ankle-foot prosthesis. We propose two control schemes to predict the amputee’s in-tended ankle position: a neural network approach and a muscle model approach. We test these approaches using EMG data measured from an amputee for several target ankle movement patterns. We find that both controllers demonstrate the ability to predict desired ankle movement patterns qualitatively. In the cur-rent implementation, the biomimetic EMG-controller demonstrates a smoother and more natural movement pat-tern than that demonstrated by the neural network approach, suggesting that a biolo-gically — motivated,model-based approach may offer certain advantages in the control of active ankle prostheses.

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