# Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

arXiv: https://arxiv.org/abs/2304.09617 · HTML: https://mlsanity.com/paper/2304.09617/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2023.

## Built on by (most cited first, 3 of 3)

- [Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots](https://mlsanity.com/paper/2601.02085/) (2026)
- [HarvestFlex: Strawberry Harvesting via Vision-Language-Action Policy Adaptation in the Wild](https://mlsanity.com/paper/2603.05982/) (2026)
- [DualLQR: Efficient Grasping of Oscillating Apples using Task Parameterized Learning from Demonstration](https://mlsanity.com/paper/2409.16957/) (2024)

## Abstract (arXiv)

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.