As physical interactions between robots and humans become more common, there is a growing need to design robots that are kinesthetically perceived as human-like. One approach to implement human-like mechanical impedance is to physically simulate models of the human neuromuscular system. However, the level of model complexity needed to achieve perception of human-like properties is unknown. The purpose of this study was to develop an objective assessment of a human subject's ability to discriminate kinesthetically between a model-defined impedance and that produced by human muscle. The assessment was based upon signal detection theory, by which the ability to discriminate between two classes of stimuli is analyzed statistically. With this assessment, we tested the hypothesis that a nonlinear muscle model is necessary to obtain perception of human-like muscle mechanical impedance. Fifteen subjects were presented with a mechanical impedance of either a simulated muscle model or electrically stimulated wrist muscle and were asked to decide if they were interacting with a “machine” or “human.” The impedances were randomized for a total of 30 presentations. The results showed that a robot that stimulates either linear viscoelastic properties or a nonlinear Hill muscle model could be distinguished from a human wrist muscle by almost all subjects. However, the subjects' ability to discriminate between the Hill model and human muscle was significantly less, which may have been due to larger overall impedance of the viscoelastic model. These results are important for the design of robots that emulates mechanical impedances that are perceived as human-like.

This special session is organized to provide an interactive forum for the introduction of a set of new curriculum modules developed under IEEE's Real World Engineering Projects (RWEP) program. The modules, which are representative of a larger collection of curriculum modules available to the public via an open-access RWEP web portal, are designed for use in the first-year engineering and computer science classroom, and are hands-on, team-based projects that emphasize the societal impact of the work that engineers do. After a brief introduction to the RWEP program and the showcased curriculum modules, the authors of the modules will work one-on-one with the audience providing tutorials on the laboratory activities associated with their modules in a highly interactive, simultaneous mode. Audience members can learn in-depth about the modules that interest them most. Each project will have its own demonstration table, with posters of information and hands-on demonstrations and/or videos. This will allow for audience members to interact directly with the authors, and to try out the same sorts of activities that are proposed for the students.

As the physical interaction of robots with humans becomes more common, there is a growing need to design robots that are tactilely perceived as humanlike. One approach of implementing a human-like mechanical impedance is to simulate models of the human neuromuscular system. However, the necessary level of complexity of these models is unknown. The goal of this study is to determine the least complex neuromuscular model which can achieve human-like mechanical impedance. We have developed an objective test of a human subject's ability to tactilely distinguish a robot and human, the kinesthetic Turing test. The results from six subjects show that a robot with linear viscoelastic properties could be distinguished from a human wrist joint. A robot simulating a nonlinear Hill muscle model was also distinguishable, but subjects' ability to discriminate was significantly less. These results are important for the design of robots emulating human-like qualities.

We have developed analog VLSI circuits to model the behavior demonstrated by biological sarcomeres, the force generating components of muscle tissue. The circuits are based upon the mathematical description of crossbridge populations developed by A.F. Huxley. We have implemented the sarcomere circuit using a standard 1.2 /spl mu/ process, and have demonstrated transient and steady-state behaviors experimentally.

Predicated execution is an effective technique for dealing with conditional branches in application programs. However, there are several problems associated with conventional compiler support for predicated execution. First, all paths of control are combined into a single path regardless of their execution frequency and size with conventional if-conversion techniques. Second, speculative execution is difficult to combine with predicated execution. In this paper, we propose the use of a new structure, referred to as the hyperblock, to overcome these problems. The hyperblock is an efficient structure to utilize predicated execution for both compiletime optimization and scheduling. Preliminary experimental results show that the hyperblock is highly effective for a wide range of superscalar and VLIW processors. 1 Introduction Superscalar and VLIW processors can potentially provide large performance improvements over their scalar predecessors by providing multiple data paths and function u...