# Learning to Prune Branches in Modern Tree-Fruit Orchards

arXiv: https://arxiv.org/abs/2507.23015 · HTML: https://mlsanity.com/paper/2507.23015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Masked World Models for Visual Control](https://mlsanity.com/paper/2206.14244/) (2022)
- [Bumblebee: A Path Towards Fully Autonomous Robotic Vine Pruning](https://mlsanity.com/paper/2112.00291/) (2021)
- [TrimBot2020: an outdoor robot for automatic gardening](https://mlsanity.com/paper/1804.01792/) (2018)
- [Precision fruit tree pruning using a learned hybrid vision/interaction controller](https://mlsanity.com/paper/2109.13162/) (2021)
- [Optical flow-based branch segmentation for complex orchard environments](https://mlsanity.com/paper/2202.13050/) (2022)
- [Uncovering implementable dormant pruning decisions from three different stakeholder perspectives](https://mlsanity.com/paper/2405.04030/) (2024)

## Built on by (most cited first, 1 of 1)

- [Visuomotor Robotic Pruning in Planar Orchards Using Hybrid Reinforcement Learning](https://mlsanity.com/paper/2609.24906/) (2026)

## Abstract (arXiv)

Dormant tree pruning is labor-intensive but essential to maintaining modern highly-productive fruit orchards. In this work we present a closed-loop visuomotor controller for robotic pruning. The controller guides the cutter through a cluttered tree environment to reach a specified cut point and ensures the cutters are perpendicular to the branch. We train the controller using a novel orchard simulation that captures the geometric distribution of branches in a target apple orchard configuration. Unlike traditional methods requiring full 3D reconstruction, our controller uses just optical flow images from a wrist-mounted camera. We deploy our learned policy in simulation and the real-world for an example V-Trellis envy tree with zero-shot transfer, achieving a 30% success rate -- approximately half the performance of an oracle planner.