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%.

翻译：示教学习为机器人学习执行农业任务，特别是选择性收获，提供了巨大潜力。其中一个挑战是目标果实在接近过程中可能处于振荡状态。抓取振荡目标有两个要求：1）在最终接近阶段紧密跟踪目标以实现无损抓取；2）完整路径应尽可能短以提高效率。我们提出了一种名为DualLQR的新方法。在该方法中，我们对移动目标使用有限时域线性二次调节器，而无需重新拟合LQR。为此，我们采用了双LQR设置，在两个独立的参考系中运行LQR。通过大量仿真测试发现，现有先进方法在无振荡时勉强满足最终精度要求，而在目标振荡时精度则低于要求。研究发现，即使在高振荡条件下，DualLQR仍能满足最终精度要求，对于高姿态振荡情况精度提升了60%。在实际苹果抓取任务中的进一步测试表明，DualLQR能够成功抓取振荡苹果，成功率高达99%。