增强型机器人采摘视角规划：利用模仿学习应对遮挡
Enhanced View Planning for Robotic Harvesting: Tackling Occlusions with Imitation Learning
AI总结：
针对机器人采摘中的遮挡问题，提出基于 ACT 模仿学习的 6-DoF 主动视角规划方法，从专家演示学习相机运动策略，并在仿真与真实农业场景中验证其高成功率、效率及跨作物泛化能力。
摘要：
在农业自动化中，固有遮挡给机器人采摘带来了重大挑战。我们提出一种新颖的基于模仿学习的视角规划方法，用于主动调整相机视角并捕获目标作物无遮挡的图像。传统视角规划器和现有基于学习的方法依赖人工设计的评估指标或奖励函数，往往难以泛化到复杂、未见场景。我们的方法采用 Action Chunking with Transformer（ACT）算法，从专家演示中学习有效的相机运动策略。这使得连续六自由度（6-DoF）视角调整更平滑、更精确，并能揭示被遮挡目标。在仿真和真实环境中开展的大量实验以农业场景以及配备 RGB-D 相机的 6-DoF 机械臂为特色，结果表明我们的方法具有更优的成功率和效率，尤其是在复杂遮挡条件下，并且能够无需重新编程即可跨不同作物泛化。本研究通过为遮挡挑战提供实用的“从演示中学习”（LfD）解决方案推进了机器人采摘，最终提升自主采摘性能和生产率。
英文摘要：
In agricultural automation, inherent occlusion presents a major challenge for robotic harvesting. We propose a novel imitation learning-based viewpoint planning approach to actively adjust camera viewpoint and capture unobstructed images of the target crop. Traditional viewpoint planners and existing learning-based methods, depend on manually designed evaluation metrics or reward functions, often struggle to generalize to complex, unseen scenarios. Our method employs the Action Chunking with Transformer (ACT) algorithm to learn effective camera motion policies from expert demonstrations. This enables continuous six-degree-of-freedom (6-DoF) viewpoint adjustments that are smoother, more precise and reveal occluded targets. Extensive experiments in both simulated and real-world environments, featuring agricultural scenarios and a 6-DoF robot arm equipped with an RGB-D camera, demonstrate our method's superior success rate and efficiency, especially in complex occlusion conditions, as well as its ability to generalize across different crops without reprogramming. This study advances robotic harvesting by providing a practical "learn from demonstration" (LfD) solution to occlusion challenges, ultimately enhancing autonomous harvesting performance and productivity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
