# 用于水培生菜机器人采收中路径安全目标到达的 Sim-to-Real 强化学习

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-22337
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：提出PSA-PPO解决水培生菜采收中受限空间安全到达问题，并在仿真与UR10e温室采收系统上验证。

# 用于水培生菜机器人采收中路径安全目标到达的 Sim-to-Real 强化学习

::: v-pre
> 原文链接：[Sim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce](https://arxiv.org/abs/2610.22337)（arXiv 2610.22337 · 发布日期：2026-10-15）

**Sim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce**

提出PSA-PPO解决水培生菜采收中受限空间安全到达问题，并在仿真与UR10e温室采收系统上验证。

## 核心要点

- 动机是在受限工作空间内实现末端执行器精确定位，满足机器人采收需求。
- 方法保留PPO裁剪目标，并融合目标条件观测、走廊空间约束、轨迹历史安全编码和任务成功准则。
- PSA-PPO全程在Isaac Lab训练，无需真机微调即可迁移到UR10e机械臂。
- 真机150个目标基准中到达率94.7%，完整温室系统144次采收成功率86.1%。

**验证范围与局限**：验证范围包括Isaac Lab仿真、UR10e真机150个目标基准及144次温室采收尝试。

## 英文摘要 (Abstract)

Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace. This study introduces the Path-Safe Approach Proximal Policy Optimization (PSA-PPO), a target-conditioned local reaching framework designed for robotic harvesting of nutrient film technique (NFT)-hydroponic lettuce in greenhouses. The proposed PSA-PPO preserves the standard clipped PPO objective while introducing a harvesting-oriented policy formulation that integrates target-conditioned observations, corridor-aware spatial constraints, trajectory-history-based safety encoding, and task-specific success criteria for constrained robotic manipulation. PSAPPO was trained entirely in Isaac Lab and successfully transferred to a physical UR10e robotic arm without any real-world policy fine-tuning. In a controlled physical benchmark against 150 scheduled targets, PSA-PPO achieved the highest reaching rate of 94.7%, outperforming Cartesian+TOTG (86.0%), RRTConnect (88.0%), and RRT* (80.0%), while reducing median RMS jerk by 39.8 to 73.0% relative to the classical planners during the final approach segment. A formulation ablation evaluated over 200 simulation targets demonstrates that path-safety and side-corridor shaping are the principal drivers of harvesting-compatible performance: removing either term substantially degrades strict success even when terminal endpoint accuracy is preserved or improved, confirming that optimization toward the endpoint alone is an insufficient objective for constrained crop-proximal manipulation. Finally, PSA-PPO was integrated with an RGB-D vision module and an end-effector and evaluated as a complete greenhouse harvesting system across 144 lettuce harvesting attempts, achieving 94.4% vision-guided target reaching and 86.1% complete harvesting success.

作者：Al Bashir, Partho Ghose, Yibin Wang, Robert G. Hardin, Joshua Peeples, Genhua Niu, Azlan Zahid
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## 原始参考来源 (Sources)

- [arXiv 2610.22337：Sim-to-Real Reinforcement Learning for Path-Safe Target Reaching in Robotic Harvesting of Hydroponic Lettuce](https://arxiv.org/abs/2610.22337)