Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Williams, Emlyn Polydoros, Athanasios |
| author_facet | Williams, Emlyn Polydoros, Athanasios |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Williams, Emlyn Polydoros, Athanasios Robotics 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. |
| title | Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.08458 |