# Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting

arXiv: https://arxiv.org/abs/2505.08458 · HTML: https://mlsanity.com/paper/2505.08458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2025.

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

- [Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement](https://mlsanity.com/paper/2607.14708/) (2026)

## Abstract (arXiv)

This paper presents a comprehensive sim-to-real pipeline for autonomous strawberry picking from dense clusters using a Franka Panda robot. Our approach leverages a custom Mujoco simulation environment that integrates domain randomization techniques. In this environment, a deep reinforcement learning agent is trained using the dormant ratio minimization algorithm. The proposed pipeline bridges low-level control with high-level perception and decision making, demonstrating promising performance in both simulation and in a real laboratory environment, laying the groundwork for successful transfer to real-world autonomous fruit harvesting.