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[2505.16547] Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation
Summary
This paper presents a zero-shot reinforcement learning framework for occlusion-aware plant manipulation, achieving high success rates in exposing target fruits in real-world scenarios.
Why It Matters
The research addresses the challenges of autonomous harvesting in agriculture, particularly in dealing with occlusions and structural uncertainties of plants. By improving the efficiency of fruit harvesting, this work has implications for agricultural robotics, potentially enhancing productivity and reducing labor costs.
Key Takeaways
- Introduces a zero-shot sim2real RL framework for plant manipulation.
- Achieves up to 86.7% success in revealing fruits despite occlusions.
- Decouples high-level planning from low-level control for better generalization.
- Demonstrates robustness across various plant types and structures.
- Addresses significant challenges in autonomous agricultural systems.
Computer Science > Robotics arXiv:2505.16547 (cs) [Submitted on 22 May 2025 (v1), last revised 23 Feb 2026 (this version, v3)] Title:Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation Authors:Nitesh Subedi, Hsin-Jung Yang, Devesh K. Jha, Soumik Sarkar View a PDF of the paper titled Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation, by Nitesh Subedi and 3 other authors View PDF HTML (experimental) Abstract:Autonomous harvesting in the open presents a complex manipulation problem. In most scenarios, an autonomous system has to deal with significant occlusion and require interaction in the presence of large structural uncertainties (every plant is different). Perceptual and modeling uncertainty make design of reliable manipulation controllers for harvesting challenging, resulting in poor performance during deployment. We present a sim2real reinforcement learning (RL) framework for occlusion-aware plant manipulation, where a policy is learned entirely in simulation to reposition stems and leaves to reveal target fruit(s). In our proposed approach, we decouple high-level kinematic planning from low-level compliant control which simplifies the sim2real transfer. This decomposition allows the learned policy to generalize across multiple plants with different stiffness and morphology. In experiments with multiple real-world plant setups, our system achieves up to 86.7% success in exposing target fruits, demonstrating robustness ...