A memory of motion for visual predictive control tasks
| Type of publication: | Conference paper |
| Citation: | Paolillo_ICRA2020_2020 |
| Publication status: | Accepted |
| Booktitle: | International Conference on Robotics and Automation |
| Year: | 2020 |
| Abstract: | This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Standard regression techniques, such as k-nearest neighbors and Gaussian process regression, are used to query the memory and provide on-line a warm-start and a way point to the control optimization process. The proposed technique allows the control scheme to achieve high performance %difficult tasks and, at the same time, keep the computational time limited. Simulation and experimental results, carried out with a 7-axis manipulator, show the effectiveness of the approach. |
| Keywords: | Visual Predictive Control |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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