DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration
<|reference_start|>DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration: Learning from Demonstration offers great potential for robots to learn to perform agricultural tasks, specifically selective harvesting. One of the challenges is that the target fruit can be oscillating while approaching. Grasping oscillating targets has two requirements: 1) close tracking of the target during the final approach for damage-free grasping, and 2) the complete path should be as short as possible for improved efficiency. We propose a new method called DualLQR. In this method, we use a finite horizon Linear Quadratic Regulator (LQR) on a moving target, without the need of refitting the LQR. To make this possible, we use a dual LQR setup, with an LQR running in two seperate reference frames. Through extensive simulation testing, it was found that the state-of-art method barely meets the required final accuracy without oscillations and drops below the required accuracy with an oscillating target. DualLQR was found to be able to meet the required final accuracy even with high oscillations, with an accuracy increase of 60% for high orientation oscillations. Further testing on a real-world apple grasping task showed that DualLQR was able to successfully grasp oscillating apples, with a success rate of 99%.<|reference_end|>
@article{van de ven2024duallqr:,
    title={DualLQR: Efficient Grasping of Oscillating Apples using Task
  Parameterized Learning from Demonstration},
    author={Robert van de Ven, Ard Nieuwenhuizen, Eldert J. van Henten, and Gert
  Kootstra},
    journal={arXiv preprint arXiv:2409.16957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16957},
    primaryClass={cs.RO}
}