# Precision fruit tree pruning using a learned hybrid vision/interaction controller

arXiv: https://arxiv.org/abs/2109.13162 · HTML: https://mlsanity.com/paper/2109.13162/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [Optical flow-based branch segmentation for complex orchard environments](https://mlsanity.com/paper/2202.13050/) (2022)
- [Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies](https://mlsanity.com/paper/2301.08387/) (2023)
- [ChicGrasp: Imitation-Learning based Customized Dual-Jaw Gripper Control for Delicate, Irregular Bio-products Manipulation](https://mlsanity.com/paper/2505.08986/) (2025)
- [Learning to Prune Branches in Modern Tree-Fruit Orchards](https://mlsanity.com/paper/2507.23015/) (2025)
- [Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning](https://mlsanity.com/paper/2609.24906/) (2026)

## Abstract (arXiv)

Robotic tree pruning requires highly precise manipulator control in order to accurately align a cutting implement with the desired pruning point at the correct angle. Simultaneously, the robot must avoid applying excessive force to rigid parts of the environment such as trees, support posts, and wires. In this paper, we propose a hybrid control system that uses a learned vision-based controller to initially align the cutter with the desired pruning point, taking in images of the environment and outputting control actions. This controller is trained entirely in simulation, but transfers easily to real trees via a neural network which transforms raw images into a simplified, segmented representation. Once contact is established, the system hands over control to an interaction controller that guides the cutter pivot point to the branch while minimizing interaction forces. With this simple, yet novel, approach we demonstrate an improvement of over 30 percentage points in accuracy over a baseline controller that uses camera depth data.