DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration
On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning
Abstract (arXiv)
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 set-up, with an LQR running in two separate 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, on the other hand, was found to be able to meet the required final accuracy even with high oscillations, while travelling the least distance. 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%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 3 of its 22 references that are in the map. First posted on arXiv 25 Sep 2024.
Builds on
3 of 22 references.
References · 3
- 2023Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changescited by 6
- 2022dPMP-Deep Probabilistic Motion Planning: A use case in Strawberry Picking Robotcited by 5
- 2023Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Controlcited by 3
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- 2026Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement
- 2026HarvestPoint-ACT: Explicit Target Selection and Harvest-Point Conditioning for Robotic Fruit Harvesting under Occlusion ↗
- 2024Autonomous Robotic Pepper Harvesting: Imitation Learning in Unstructured Agricultural Environments ↗
- 2026Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning
- 2026From Vision to Harvest: Benchmarking Vision-Language Models for Multi-Arm Robotic Fruit Harvesting
- 2025Advances on Affordable Hardware Platforms for Human Demonstration Acquisition in Agricultural Applications ↗
- 2025Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation
- 2023Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration