DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration

2024-09-25

arXiv.org

AI Summary

This work addresses the challenge of achieving high-precision, short-path, and non-damaging fruit grasping by agricultural robots under high-frequency fruit oscillation. We propose a dual-reference-frame finite-horizon Linear Quadratic Regulator (LQR) control framework that integrates motion target modeling, task-parameterized teaching-by-demonstration, and real-time state feedback—enabling robust tracking of oscillating targets without online re-optimization. Compared to conventional approaches, our method overcomes the longstanding trade-off between accuracy and efficiency in oscillatory environments: simulations demonstrate sub-millimeter positioning accuracy with minimal path length under high-frequency vibration; physical apple harvesting achieves a 99% success rate, significantly outperforming state-of-the-art methods. The core innovation lies in the first-ever dual-coordinate-system LQR architecture, establishing a generalizable, computationally efficient real-time control paradigm for dynamic agricultural manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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%.

Problem

• Grasping oscillating apples efficiently
• Minimizing path length for grasping
• Ensuring damage-free grasping accuracy

Innovation

• DualLQR for oscillating targets
• Dual LQR in two frames
• Efficient, accurate apple grasping