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@INPROCEEDINGS{Paolillo_ICRA2020_2020,
         author = {Paolillo, Antonio and Lembono, Teguh Santoso and Calinon, Sylvain},
       keywords = {Visual Predictive Control},
       projects = {Idiap},
          title = {A memory of motion for visual predictive control tasks},
      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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2020/Paolillo_ICRA2020_2020.pdf}
}