Compact, Lightweight Servo-Controllable Brakes

Compact, lightweight servo-controllable brakes capable of high torques are being developed for incorporation into robot joints. A brake of this type is based partly on the capstan effect of tension elements. In a brake of the type under development, a controllable intermediate state of torque is reached through on/off switching at a high frequency

Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps

We present a novel and simple experimental method called Physical Human Interactive Guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose while the robot’s configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the Physical Human Interactive Guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object’s principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests shows that grasps with low skewness were significantly more robust than grasps with high skewness (77% to 93%). We conclude with a detailed discussion of how the Physical Human Interactive Guidance method relates to existing methods for extracting the human principles for physical interaction.Keywords: Haptic interfaces, Human robot interaction, Manipulators, Grasping, Telerobotic

Repeated Training with Augmentative Vibrotactile Feedback Increases Object Manipulation Performance

Most users of prosthetic hands must rely on visual feedback alone, which requires visual attention and cognitive resources. Providing haptic feedback of variables relevant to manipulation, such as contact force, may thus improve the usability of prosthetic hands for tasks of daily living. Vibrotactile stimulation was explored as a feedback modality in ten unimpaired participants across eight sessions in a two-week period. Participants used their right index finger to perform a virtual object manipulation task with both visual and augmentative vibrotactile feedback related to force. Through repeated training, participants were able to learn to use the vibrotactile feedback to significantly improve object manipulation. Removal of vibrotactile feedback in session 8 significantly reduced task performance. These results suggest that vibrotactile feedback paired with training may enhance the manipulation ability of prosthetic hand users without the need for more invasive strategies

Embodiment and Manipulation Learning Process for a Humanoid Hand

Babies are born with simple manipulation capabilities such as reflexes to perceived stimuli. Initial discoveries by babies are accidental until they become coordinated and curious enough to actively investigate their surroundings. This thesis explores the development of such primitive learning systems using an embodied light-weight hand with three fingers and a thumb. It is self-contained having four motors and 36 exteroceptor and proprioceptor sensors controlled by an on-palm microcontroller. Primitive manipulation is learned from sensory inputs using competitive learning, back-propagation algorithm and reinforcement learning strategies. This hand will be used for a humanoid being developed at the MIT Artificial Intelligence Laboratory

Models of generalization in motor control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 207-215).Motor learning for humans is based on the capacity of the central nervous system (CNS) to perform computation and build an internal model for a task. This thesis investigates the CNS's ability to generalize a learned motor skill throughout neighboring spatial locations, its ability to divide the spatial general­ization with variation of context, and proposes models of how these generalizations might be implemented. The investigation involved human psychophysics and simulations. The experi­mental paradigm was to study human neuromuscular adaptation to viscous force perturbation. When external perturbations were applied to the hand during a reach­ing task, the movement became distorted. This distortion motivated the CNS to produce counterbalancing forces, which resulted in the modification of the internal model for the task. Experimental results indicated that the introduction of interfering perturbations near the trained location disturbed the learned skill. In addition, if the same move­ment was perturbed in two opposite directions in sequence, neither of the forces are learned. Conversely, the adaptation to two opposite forces was possible within the same space when the forces were applied to two contextually distinguished movements. This was possible only when these movements were interleaved fairly regularly. During the adaptation to a difficult task, such as contextual distinction in the same spatial location, humans often used other strategies to avoid learning the actual paradigm. These strategies allowed subjects to perform the task -- without changing their internal models appropriately, and thus this was also investigated as a part of the learning process. Finally, a multiple function model was constructed which allowed multiple contex­tually dependent functions to co-exist within one state space. The sensory feedback affected all functions, however, only one function was active to output a motor com­mand. This model supported the experimental data presented. The results of the psychophysical experiments as well as an explanation of the simulations and models that were developed will be presented in this thesis.by Yoky Matsuoka.Ph.D