用虚拟环境训练叉车机器人，无需真实数据即能直接在实体机器上成功作业。
Visual-Based Forklift Learning System Enabling Zero-Shot Sim2Real Without Real-World Data
- 基于视觉的端到端强化学习，利用高保真数字环境训练。
- 真实机器人实验中托盘装载任务成功率达60%。
- 零样本跨域迁移，适合工业自动化研究者参考。
叉车广泛应用于各类工业场景，其中平衡重式叉车应用多样但自动化进展缓慢，主要受限于缺乏安全且可验证性能的开发环境。本文提出一种结合光栅化数字学习环境与1/14比例机器人叉车系统的学习系统。受叉车司机培训方式启发，采用基于视觉的端到端深度强化学习方法，在仅使用CAD数据构建的数字化环境中进行训练，确保安全且无需真实世界数据。同时，通过配置与真实叉车相似的1/14比例机器人，在物理环境中安全验证该方法。真实实验中，机器人在托盘装载任务上达到60%的成功率。该方法实现了无需额外启发式设计的零样本模拟到现实迁移，为平衡重式叉车自动化提供了首个学习驱动范例。
原文摘要 · Abstract (English)
Forklifts are used extensively in various industrial settings and are in high demand for automation. In particular, counterbalance forklifts are highly versatile and employed in diverse scenarios. However, efforts to automate these processes are lacking, primarily owing to the absence of a safe and performance-verifiable development environment. This study proposes a learning system that combines a photorealistic digital learning environment with a 1/14-scale robotic forklift environment to address this challenge. Inspired by the training-based learning approach adopted by forklift operators, we employ an end-to-end vision-based deep reinforcement learning approach. The learning is conducted in a digitalized environment created from CAD data, making it safe and eliminating the need for real-world data. In addition, we safely validate the method in a physical setting utilizing a 1/14-scale robotic forklift with a configuration similar to that of a real forklift. We achieved a 60% success rate in pallet loading tasks in real experiments using a robotic forklift. Our approach demonstrates zero-shot sim2real with a simple method that does not require heuristic additions. This learning-based approach is considered a first step towards the automation of counterbalance forklifts.
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