A Novel Telerobotic Method for Human-In-The-Loop Assisted Grasping Based on Intention Recognition
Document Type
Conference Proceeding
Publication Date
2014
Digital Object Identifier (DOI)
https://doi.org/10.1109/ICRA.2014.6907556
Abstract
In this work, we present a methodology for enabling a robot to identify an object and grasp configuration of interest and assist the human teleoperating the robot, to grasp the object. The identification is carried out in real-time by detecting the motion intention of the human as they are teleoperating the remote robotic arm towards the object and the grasp configuration. Simultaneously, depending on the detected object and grasp configuration, the human user is assisted to translate and orient the remote arm gripper in order to preshape and grasp the object. The complete process occurs with the human teleoperating the arm, and without them having to interact with another interface. Motion intention recognition is carried out by using Hidden Markov Models (HMMs), trained offline by preshape trials performed by a skilled teleoperator. The environment is unstructured and comprises of a number of objects, each with multiple grasp configurations. Experimental tests on healthy human subjects have validated our intention recognition based assistance method. They show that the method allows objects to be grasped and placed 48% faster, and with much ease compared to unassisted teleoperation. Moreover, we have proved that the model for intention recognition, trained by a skilled teleoperator, can be used by novice users to efficiently execute a grasping task in teleoperation.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
2014 IEEE International Conference on Robotics and Automation (ICRA), p. 4762-4769
Scholar Commons Citation
Khokar, Karan; Alqasemi, Redwan; Sarkar, Sudeep; Reed, Kyle B.; and Dubey, Rajiv, "A Novel Telerobotic Method for Human-In-The-Loop Assisted Grasping Based on Intention Recognition" (2014). Mechanical Engineering Faculty Publications. 96.
https://digitalcommons.usf.edu/egr_facpub/96
