The sense of touch, which is essential for human dexterity, is virtually absent from today's robotic hands. In this work we present progress in creating a highly-dexterous bimanual tactile telerobot, and evaluate its performance compared to bare human hands. The system, consisting of anthropomorphic robot hands, biomimetic tactile sensors, and advanced haptic gloves, enables a human operator to intuitively control and feel what the robotic hands are touching. Through carefully tuned tactile and kinematic mapping it was possible to intuitively perform dexterous operations, including pick and place tasks and even in-hand manipulation, a challenge for most autonomous robotic hands. Performance of the system was evaluated in standard measures of human and robotic dexterity such as the Box and Block test and other YCB benchmarks. This first-generation telerobot was found to have promising performance with the pilot able to do the same tasks in the telerobot between 1/4th to 1/12th the speed of their bare hands depending on the task complexity.

Myoelectric prosthetic hand users have difficulty with, and frequently avoid, grasping fragile objects with their prosthesis. While the sense of touch is known to be critical for human hand dexterity, it has been virtually absent in prosthetic hands. In this study, a standard myoelectric prosthetic hand was modified with tactile sensors and a simple tactile reflex to inhibit excessive forces on contact. The tactile sensors were made from an open-cell self-skinning polyurethane foam that produced a detectable increase in air pressure inside the foam when contacted. This contact signal was then used by an inhibitory reflex controller which served to reduce the gain of weaker closing signals after contact but allow stronger closing signals to pass through. Four unilateral myoelectric prosthesis users completed five trials of three different timed grasping tasks with fragile and rigid items. Subjects performed each task in three different scenarios: with their sound side limb, their current myoelectric hand, and the modified prosthesis with tactile reflex. Findings demonstrated that grasping performance with fragile objects was significantly enhanced using the modified prosthesis, even nearing the performance of subject's sound side limb. Results suggest that this approach can substantially improve the speed and success of grasping fragile items, leading to improved use patterns, decreased cognitive effort, and improved user confidence.

In order to endow robots with human-like tactile sensory abilities, they must be provided with tactile sensors and intelligent algorithms to select and control useful exploratory movements and interpret data from all available sensors. Current robotic systems do not possess such sensors or algorithms. In this study we integrate multimodal tactile sensing (force, vibration and temperature) from the BioTac® with a Shadow Dexterous Hand and program the robot to make exploratory movements similar to those humans make when identifying objects by their compliance, texture, and thermal properties. Signal processing strategies were developed to provide measures of these perceptual properties. When identifying an object, exploratory movements are intelligently selected using a process we have previously developed called Bayesian exploration [1], whereby exploratory movements that provide the most disambiguation between likely candidates of objects are automatically selected. The exploration algorithm was augmented with reinforcement learning whereby its internal representations of objects evolved according to its cumulative experience with them. This allowed the algorithm to compensate for drift in the performance of the anthropomorphic robot hand and the ambient conditions of testing, improving accuracy while reducing the number of exploratory movements required to identify an object. The robot correctly identified 10 different objects on 99 out of 100 presentations.

Tactile sensations make grasping fragile objects a simple and unchallenging task for the human hand. Prosthetic hands lack these sensory capabilities and their users frequently struggle with such tasks. To address this problem, the benefits of compliant fingertips with contact-detection reflexes were assessed in one prosthesis user when grasping fragile objects (eggshells, foam packing peanuts, crackers, and soft clay). A commercially available myoelectric prosthetic hand was modified to include the compliant BioTac® tactile sensors (SynTouch, LLC), which have previously been demonstrated to be more sensitive to contact than the human fingertip. Upon sensing contact during hand closure on an object, the gain of the operator's EMG command signals to the prosthesis' motor was reduced to prevent excessive closing forces, a behavior similar to an inhibitory reflex. This allowed the prosthetic hand to quickly react to the presence of the object and permitted the operator to handle fragile objects with ease and without the usual dependency on visual feedback. The time required to grasp and move a set of fragile objects with this modified prosthesis was compared to the subject's usual prosthetic hand. The contact detection method demonstrated both utility and reliability through faster completion times and reduced variance in the times to complete these trials.