エッジで動作するRoboSense：遠隔操作向けロボットハンドにおける滑り・しわ・物体形状の検出
RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations
ロボットハンドの力・トルクと関節角度から、把持物体の滑り・しわ・形状を機械学習で検出し、遠隔操作の遅延を低減する手法を提案した。
著者: Sudev Kumar Padhi, Mohit Kumar, Debanka Giri, Subidh Ali
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Slip and crumple detection is essential for performing robust manipulation tasks with a robotic hand (RH) like remote surgery. It has been one of the challenging problems in the robotics manipulation community. In this work, we propose a technique based on machine learning (ML) based techniques to detect the slip, and crumple as well as the shape of an object that is currently held in the robotic hand. We proposed ML model will detect the slip, crumple, and shape using the force/torque exerted and the angular positions of the actuators present in the RH. The proposed model would be integrated into the loop of a robotic hand(RH) and haptic glove(HG). This would help us to reduce the latency in case of teleoperation