Name
Title | ||
|---|---|---|
Deep Correspondence Learning for Effective Robotic Teleoperation using Virtual Reality |
Abstract | ||
|---|---|---|
By projecting into a 3-D workspace, robotic teleoperation using virtual reality allows for a more intuitive method of control for the operator than using a 2-D view from the robot's visual sensors. This paper investigates a setup that places the teleoperator in a virtual representation of the robot's environment and develops a deep learning based architecture modeling the correspondence between the operator's movements in the virtual space and joint angles for a humanoid robot using data collected from a series of demonstrations. We evaluate the correspondence model's performance in a pick-and - place teleoperation experiment. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/Humanoids43949.2019.9035031 | 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) |
Keywords | DocType | ISSN |
robotic teleoperation,virtual reality,virtual representation,deep learning based architecture,virtual space,humanoid robot,correspondence model,deep correspondence learning,robot visual sensors,3-D workspace,pick-and-place teleoperation | Conference | 2164-0572 |
ISBN | Citations | PageRank |
978-1-5386-7631-8 | 1 | 0.36 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanket Gaurav | 1 | 2 | 1.05 |
| Zainab Al-Qurashi | 2 | 1 | 0.36 |
| Amey Barapatre | 3 | 1 | 0.36 |
| George Maratos | 4 | 1 | 0.36 |
| Tejas Sarma | 5 | 1 | 0.36 |
| Brian D. Ziebart | 6 | 981 | 59.95 |