arXiv Open Access 2025

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

Emlyn Williams Athanasios Polydoros

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Abstrak

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.

Topik & Kata Kunci

cs.RO

Penulis (2)

Emlyn Williams

Athanasios Polydoros

Format Sitasi

APA MLA BibTeX

Williams, E., Polydoros, A. (2025). Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting. https://arxiv.org/abs/2505.08458

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓