Comparing Robot Grasping Teleoperation Across Desktop and Virtual Reality with ROS Reality
By David Whitney, Eric Rosen, Elizabeth Phillips, George Dimitri Konidaris, Stefanie Tellex. 2017
175 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Mechanical Engineering, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Li Fei-Fei
- Luc Van Gool
- Jitendra Malik
- Abhinav Kumar Gupta
- Silvio Savarese
- Trevor Darrell
- Kristen Grauman
- Xiaogang Wang
In Mechanical Engineering
- Li Fei-Fei
- Yuke Zhu
- Silvio Savarese
- Danfei Xu
- Ajay Mandlekar
- Sonia Chernova
- Aude Gemma Billard
- Athanasios S. Polydoros
In Human-Computer Interaction
- Silvio Savarese
- Aude Gemma Billard
- Animesh Garg
- De-An Huang
- Li Fei-Fei
- Ajay Mandlekar
- Juan Carlos Niebles
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].”
- Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs (2018, methodology): “To demonstrate the actions required for a task, a robot could be physically guided or operated remotely (1, 40).”
- Neural Task Graphs: Generalizing to Unseen Tasks From a Single Video Demonstration (2018, background): “Traditional imitation learning work uses physical guidance [1, 31] or teleoperation [39, 43] as demonstration.”
- Recent Advances in Robot Learning from Demonstration (2020, background): “Teleoperation is another widely used demonstration input and has been applied to trajectory learning (21), task learning (22), grasping (23), and high-level tasks (24).”