PolarNet：用于语言引导机器人操作的3D点云
PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
AI总结：
针对2D图像表示难以融合多视角并推断3D关系的问题，本文提出PolarNet，用点云编码器与多模态transformer融合语言和3D信息，在RLBench及真实机器人上取得优于现有方法的表现。
摘要：
机器人理解并执行基于自然语言指令的操作任务的能力，是机器人学的一个长期目标。语言引导操作的主流方法使用2D图像表示，这类方法在融合多视角相机以及推断精确3D位置和关系方面面临困难。为解决这些局限，我们提出一种名为PolarNet的基于3D点云的策略，用于语言引导操作。它利用精心设计的点云输入、高效的点云编码器和多模态transformer来学习3D点云表示，并将其与语言指令整合以进行动作预测。在RLBench基准上开展的多种实验表明，PolarNet有效且具有数据效率。它在单任务和多任务学习中均优于最先进的2D和3D方法。它还在真实机器人上取得了有前景的结果。
英文摘要：
The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-guided manipulation use 2D image representations, which face difficulties in combining multi-view cameras and inferring precise 3D positions and relationships. To address these limitations, we propose a 3D point cloud based policy called PolarNet for language-guided manipulation. It leverages carefully designed point cloud inputs, efficient point cloud encoders, and multimodal transformers to learn 3D point cloud representations and integrate them with language instructions for action prediction. PolarNet is shown to be effective and data efficient in a variety of experiments conducted on the RLBench benchmark. It outperforms state-of-the-art 2D and 3D approaches in both single-task and multi-task learning. It also achieves promising results on a real robot.
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。
