GP3：一种面向机器人操作、利用多视角图像的三维几何感知策略
GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation
AI总结：
本文提出GP3策略，利用多视角RGB图像和空间编码器推断三维几何表示，并结合语言指令生成连续动作，在模拟与真实机器人上均展现出优于现有方法的几何感知操作能力。
摘要：
有效的机器人操作依赖于对三维场景几何的精确理解，而获取此类几何信息最直接的方式之一是通过多视角观测。受此启发，我们提出GP3——一种利用多视角输入的三维几何感知机器人操作策略。GP3采用一个空间编码器从RGB观测中推断稠密空间特征，这些特征能够估计深度和相机参数，从而形成一种紧凑且富有表现力、专为操作任务定制的三维场景表示。该表示与语言指令融合，并通过一个轻量级策略头转换为连续动作。综合实验表明，GP3在模拟基准上持续优于当前最先进的方法。此外，GP3能够有效迁移到没有深度传感器或预建地图的真实机器人上，仅需极少的微调。这些结果凸显了GP3作为一种实用、与传感器无关的几何感知机器人操作解决方案的价值。
英文摘要：
Effective robotic manipulation relies on a precise understanding of 3D scene geometry, and one of the most straightforward ways to acquire such geometry is through multi-view observations. Motivated by this, we present GP3 -- a 3D geometry-aware robotic manipulation policy that leverages multi-view input. GP3 employs a spatial encoder to infer dense spatial features from RGB observations, which enable the estimation of depth and camera parameters, leading to a compact yet expressive 3D scene representation tailored for manipulation. This representation is fused with language instructions and translated into continuous actions via a lightweight policy head. Comprehensive experiments demonstrate that GP3 consistently outperforms state-of-the-art methods on simulated benchmarks. Furthermore, GP3 transfers effectively to real-world robots without depth sensors or pre-mapped environments, requiring only minimal fine-tuning. These results highlight GP3 as a practical, sensor-agnostic solution for geometry-aware robotic manipulation.
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。
