2019/07/23 by Masoud Baghbahari, Baghbahari, Masoud, Aman Behal +1
Computer Science · Engineering · Mathematics · Neuroscience · #Advanced Sensor and Energy Harvesting Materials #Artificial intelligence #Calibration #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #Mathematics #Robot #Robot Manipulation and Learning #Robotics (cs.RO) #Statistics #Tactile and Sensory Interactions #Tactile sensor #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.1907.09656
arxiv created 2019/07/23 · openalex publication_date 2019/07/23 · arxiv updated 2019/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Tactile perception is an essential ability of intelligent robots in interaction with their surrounding environments. This perception as an intermediate level acts between sensation and action and has to be defined properly to generate suitable action in response to sensed data. In this paper, we propose a feedback approach to address robot grasping task using force-torque tactile sensing. While visual perception is an essential part for gross reaching, constant utilization of this sensing modality can negatively affect the grasping process with overwhelming computation. In such case, human being utilizes tactile sensing to interact with objects. Inspired by, the proposed approach is presented and evaluated on a real robot to demonstrate the effectiveness of the suggested framework. Moreover, we utilize a deep learning framework called Deep Calibration in order to eliminate the effect of bias in the collected data from the robot sensors.