# Safe Leaf Manipulation for Accurate Shape and Pose Estimation of Occluded Fruits

arXiv: https://arxiv.org/abs/2409.17389 · HTML: https://mlsanity.com/paper/2409.17389/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [DM-OSVP++: One-Shot View Planning Using 3D Diffusion Models for Active RGB-Based Object Reconstruction](https://mlsanity.com/paper/2504.11674/) (2025)
- [Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation](https://mlsanity.com/paper/2505.16547/) (2025)
- [Deformable Cluster Manipulation via Whole-Arm Policy Learning](https://mlsanity.com/paper/2507.17085/) (2025)
- [SG-DOR: Learning Scene Graphs with Direction-Conditioned Occlusion Reasoning for Pepper Plants](https://mlsanity.com/paper/2603.06512/) (2026)

## Abstract (arXiv)

Fruit monitoring plays an important role in crop management, and rising global fruit consumption combined with labor shortages necessitates automated monitoring with robots. However, occlusions from plant foliage often hinder accurate shape and pose estimation. Therefore, we propose an active fruit shape and pose estimation method that physically manipulates occluding leaves to reveal hidden fruits. This paper introduces a framework that plans robot actions to maximize visibility and minimize leaf damage. We developed a novel scene-consistent shape completion technique to improve fruit estimation under heavy occlusion and utilize a perception-driven deformation graph model to predict leaf deformation during planning. Experiments on artificial and real sweet pepper plants demonstrate that our method enables robots to safely move leaves aside, exposing fruits for accurate shape and pose estimation, outperforming baseline methods. Project page: https://shaoxiongyao.github.io/lmap-ssc/.