让机器人在复杂植物中精准暴露果实，实现零样本真实世界操作。
Find the Fruit: Zero-Shot Sim2Real RL for Occlusion-Aware Plant Manipulation
- 分层控制：高层规划移叶露果，底层柔顺控制提升适应性。
- 真实场景下果实暴露成功率最高达86.7%，抗遮挡与植株差异能力强。
- 无需真实数据训练，适用于多种形态的植物采摘任务。
开放环境中的自主采摘是一项复杂的操作挑战。多数情况下，自主系统需应对严重遮挡，并在结构不确定性大（每株植物各不相同）的条件下进行交互。感知与建模不确定性使得可靠采摘控制器的设计变得困难，导致部署时性能不佳。本文提出一种面向遮挡感知的植物操作的仿真到现实强化学习框架，策略完全在仿真中学习，通过重新定位茎叶以暴露目标果实。所提方法将高层运动规划与底层柔顺控制解耦，简化了仿真到现实的迁移过程。该分解使学习到的策略能跨不同刚度和形态的多类植物泛化。在多个真实植物场景下的实验表明，系统在暴露目标果实方面的成功率最高可达86.7%，展现出对遮挡变化和结构不确定性的鲁棒性。
原文摘要 · Abstract (English)
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 to occlusion variation and structural uncertainty.
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