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Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting
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Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting
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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.
Forward citations
Cited by 2 Pith papers
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Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement
A single PPO policy, trained with domain randomization and heuristic phase gating, performs obstacle separation, detachment, and placement, reaching 82% real-world strawberry-harvest success with zero-shot sim-to-real...
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Vision-Based Obstacle Separation for Strawberry Harvesting in Clusters Using Hierarchical Reinforcement Learning
A hierarchical reinforcement-learning harvester that separates obstacle strawberries before grasping improves real-world strawberry-picking success from 59.7% to 80.0% versus direct picking, at a cost of 1.22 s extra ...
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