Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot
By Jacky Liang, Jeffrey Mahler, Michael Laskey, Pusong Li, Ken Goldberg. 2017
28 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Control and Systems Engineering
- Yuke Zhu
- Li Fei-Fei
- Ajay Mandlekar
- Silvio Savarese
- Animesh Garg
- Daniela Rus
- Joseph DelPreto
- Anchit Gupta
In Mechanical Engineering
Citations in the corpus
- ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018, background): “…supervision can be provided through contemporary game interfaces such as a keyboard and mouse [26, 27], a video game controller [28], a 3D-mouse [29, 30], special purpose master-slave interfaces[31, 32], or through free-space positioning interfaces such as virtual reality (VR) [33, 34, 35].”
- Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation (2020, methodology): “Incorporating automation into teleoperation has been a key method for improving operator efficiency [6]; using deep learning has enabled automated grasping [8], knot tying [9], and cloth folding [10].”
- GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators (2023, unknown): “…and enabled impressive robot capabilities in a diverse range of challenging situations such as fine grain manipulation of everyday tasks [9], robot surgery [10], [11], [12], underwater exploration [13], [14], and disaster relief [15], [16], all of which would be useful skills for a robot to learn.”