The sensorimotor integration system can be viewed as an observer attempting to estimate its own state and the state of the environment by integrating multiple sources of information. We describe a computational framework capturing this notion, and some specific models of integration and adaptation that result from it. Psychophysical results from two sensorimotor systems, subserving the integration and adaptation of visuo-auditory maps, and estimation of the state of the hand during arm movements, are presented and analyzed within this framework. These results suggest that: (1) Spatial information from visual and auditory systems is integrated so as to reduce the variance in localization. (2) The effects of a remapping in the relation between visual and auditory space can be predicted from a simple learning rule. (3) The temporal propagation of errors in estimating the hand's state is captured by a linear dynamic observer, providing evidence for the existence of an intern...

Contents: 46 pages, including 1 appendix, 1 table, and 16 gures.

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We apply manipulation planning to computer animation. A new path planner is presented that automatically computes the collision-free trajectories for several cooperating arms to manipulate a movable object between two configurations. This implemented planner is capable of dealing with complicated tasks where regrasping is involved. In addition, we present a new inverse kinematics algorithm for the human arms. This algorithm is utilized by the planner for the generation of realistic human arm motions as they manipulate objects. We view our system as a tool for facilitating the production of animation.

Abstract. We present a new method for representing human movement compactly, in terms of a linear superimposition of simpler movements termed primitives. This method is a part of a larger research project aimed at modeling motor control and imitation using the notion of perceptuo-motor primitives, a basis set of coupled perceptual and motor routines. In our model, the perceptual system is biased by the set of motor behaviors the agent can execute, so it automatically classifies observed movements into its executable repertoire. In this paper, we describe a method for automatically deriving a set of primitives directly from human movement data. We used data from a psychophysical experiment on human imitation to derive a set of primitives, and then used those primitives as a basis for superposition and sequencing to reconstruct the original movements. We performed principal component analysis on segments from these data, resulting in a set of basis vectors. Next we clustered in the space of projections of segments onto the eigenvectors, to obtain a set of frequently used movements. To validate the approach experimentally, we used the movement obtained by expanding the cluster points in terms of the eigenvectors as a sequence of via points to control a humanoid dynamic simulation. We also developed an error metric to measure the effectiveness of the process. 1

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Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.

In this paper, the current “state of the art” for virtual reality (VR) applications in the field of motor rehabilitation is reviewed. The paper begins with a brief overview of available equipment options. Next, a discussion of the scientific rationale for use of VR in motor rehabilitation is provided. Finally, the major portion of the paper describes the various VR systems that have been developed for use with patients, and the results of clinical studies reported to date in the literature. Areas covered include stroke rehabilitation (upper and lower extremity training, spatial and perceptual-motor training), acquired brain injury, Parkinson’s disease, orthopedic rehabilitation, balance training, wheelchair mobility and functional activities of daily living training, and the newly developing field of telerehabilitation. Four major findings emerge from these studies: (1) people with disabilities appear capable of motor learning within virtual environments; (2) movements learned by people with disabilities in VR transfer to real world equivalent motor tasks in most cases, and in some cases even generalize to other untrained tasks; (3) in the few studies (n = 5) that have compared motor learning in real versus virtual environments, some advantage for VR training has been found in all cases; and (4) no occurrences of cybersickness in impaired populations have been reported to date in experiments where VR has been used to train motor abilities.

During reaching movements, the brain’s internal models map desired limb motion into predicted forces. When the forces in the task change, these models adapt. Adaptation is guided by generalization: errors in one movement influence prediction in other types of movement. If the mapping is accomplished with population coding, combining basis elements that encode different regions of movement space, then generalization can reveal the encoding of the basis elements. We present a theory that relates encoding to generalization using trial-by-trial changes in behavior during adaptation. We consider adaptation during reaching movements in various velocity-dependent force fields and quantify how errors generalize across direction. We find that the measurement of error is critical to the theory. A typical assumption in motor control is that error is the difference between a current trajectory and a desired trajectory (DJ) that does not change during adaptation. Under this assumption, in all force fields that we examined, including one in which force randomly changes from trial to trial, we found a bimodal generalization pattern, perhaps reflecting basis elements that encode direction bimodally. If the DJ was allowed to vary, bimodality was reduced or eliminated, but the generalization function accounted for nearly twice as much variance. We suggest, therefore, that basis elements representing the internal model of dynamics are sensitive to limb velocity with bimodal tuning; however, it is also possible that during adaptation the error metric itself adapts, which affects the implied shape of the basis elements.

We present an algorithm for computing stable collision-free motions for humanoid robots given fullbody posture goals. The motion planner is part of a simulation environment under development for providing high-level software control for humanoid robots. Given a robot's internal model of the environment and a statically-stable desired posture, we use a randomized path planner to search the configuration space of the robot for a collision-free path. Balance constraints are imposed on incremental search motions in order to maintain the overall dynamic stability of the computed trajectories. The algorithm is presented along with preliminary results using an experimental implementation on a dynamic model of the H5 humanoid robot.