An Integrated System for Dextrous Manipulation

This paper describes an integrated system for dextrous manipulation using a Utah-MIT hand that allows one to look at the higher levels of control in a number of grasping and manipulation tasks. The system consists of a number of low-level system primitives for grasping, integrated hand and robotic arm movement, tactile sensors mounted on the fingertips, sensing primitives to utilize joint position, tendon force and tactile array feedback, and a high-level programming environment that allows task level scripts to be created for grasping and manipulation tasks. A number of grasping and manipulation tasks are described that have been implemented with this system

Grasping Strategy and Control Algorithm of Two Robotic Fingers Equipped with Optical Three-Axis Tactile Sensors

AbstractThis paper presents grasping strategy of robot fingers based on tactile sensing information acquired by optical three-axis tactile sensor. We developed a novel optical three-axis tactile sensor system based on an optical waveguide transduction method capable of acquiring normal and shearing forces. The sensors are mounted on fingertips of two robotic fingers. To enhance the ability of recognizing and manipulating objects, we designed the robot control system architecture comprised of connection module, thinking routines, and a hand/finger control modules. We proposed tactile sensing-based control algorithm in the robot finger control system to control fingertips movements by defining optimum grasp pressure and perform re-push movement when slippage was detected. Verification experiments were conducted whose results revealed that the finger's system managed to recognize the stiffness of unknown objects and complied with sudden changes of the object's weight during object manipulation tasks

GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger

This work describes the development of a high-resolution tactile-sensing finger for robot grasping. This finger, inspired by previous GelSight sensing techniques, features an integration that is slimmer, more robust, and with more homogeneous output than previous vision-based tactile sensors. To achieve a compact integration, we redesign the optical path from illumination source to camera by combining light guides and an arrangement of mirror reflections. We parameterize the optical path with geometric design variables and describe the tradeoffs between the finger thickness, the depth of field of the camera, and the size of the tactile sensing area. The sensor sustains the wear from continuous use -- and abuse -- in grasping tasks by combining tougher materials for the compliant soft gel, a textured fabric skin, a structurally rigid body, and a calibration process that maintains homogeneous illumination and contrast of the tactile images during use. Finally, we evaluate the sensor's durability along four metrics that track the signal quality during more than 3000 grasping experiments.Comment: RA-L Pre-print. 8 page

More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at (i) estimating grasp adjustment outcomes, (ii) selecting efficient grasp adjustments for quick grasping, and (iii) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL). Website: https://sites.google.com/view/more-than-a-feelin