Remote teleoperation has shown significant advancements since the first teleoperation system was proposed by Goertz in the 1940s. In recent years, the research on shared control methodologies in which the robot assists the operators in accomplishing the desired tasks has gained extensive attention. One such important task in teleoperation is object grasping. In this paper, we propose a shared control framework to enhance the teleoperated grasping performance. The proposed framework is built upon a virtual reality device-based direct teleoperation system. In this framework, a template matching-based object point cloud compensation is introduced for multi-angle grasping pose generation. Then, the feasible grasping candidates are selected considering joint constraints-aware manipulability. Finally, the grasping assistance is achieved by trajectory blending with dynamic authority adjustment. To validate the performance of the proposed framework, we carried out experimental evaluations. The output results indicate improved grasping performance in terms of reduced task completion time, linear trajectory, and workload.

Vision-based tactile sensors have gained extensive attention in the robotics community. The sensors are highly expected to be capable of extracting contact information i.e. haptic information during in-hand manipulation. This nature of tactile sensors makes them a perfect match for haptic feedback applications. In this paper, we propose a contact force estimation method using the vision-based tactile sensor DIGIT [1], and apply it to a position-force teleoperation architecture for force feedback. The force estimation is carried out by (1) building a depth map for DIGIT gel's surface deformation measurement, and (2) applying a regression algorithm on estimated depth data and ground truth force data to get the depth-force relationship. The experiment is performed by constructing a grasping force feedback system with a haptic device as a leader robot and a parallel robot gripper as a follower robot, where the DIGIT sensor is attached to the tip of the robot gripper to estimate the contact force. The preliminary results show the capability of using the low-cost vision-based sensor for force feedback applications.