超越相对几何：面向机器人的度量感知几何感知
Beyond Relative Geometry: Metric-Aware Geometry Perception for Robotics
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中文总结 AI 辅助
该研究针对现有机器人几何感知仅能重建任意尺度相对几何的局限，提出端到端即插即用框架MAGP，在保持相对几何精度的同时大幅降低绝对误差，集成后可显著提升多个机器人策略的性能。
中文摘要 AI 辅助
近期的具身模型越来越多地利用几何表示来提升空间推理和机器人操作能力。然而，现有重建方法仅能重建具有任意尺度的相对几何，导致预测的物体尺寸和空间距离会随场景、视角及输入配置发生变化。这种不一致性使得几何感知无法直接与基于真实世界尺度定义的机器人动作对齐。为解决这一局限，我们提出度量感知几何感知（Metric-Aware Geometry Perception，MAGP），这是一种端到端、即插即用的度量几何重建框架，可无缝集成到机器人策略中。其核心设计包括：度量尺度等变增强（Metric Scale Equivariant Augmentation），该设计鼓励模型从相机参数和深度观测中重建度量几何，确保重建的几何遵循观测指定的度量尺度；灵活的度量条件（Flexible Metric Conditioning），该设计进一步使MAGP支持任意视角数量以及相机与深度输入的组合，提升了对异构机器人感知配置的鲁棒性。这些设计共同产生了几何一致的重建结果，在不同场景和感知条件下具有稳定的物体尺寸和空间距离。在ETH3D、MegaDepth和ScanNet++上的实验表明，MAGP在保持相对几何精度的同时，将绝对误差降低了一个数量级以上，从2.01米降至0.07米。当集成到多个机器人策略中时，MAGP在LIBERO、RoboTwin和零样本LIBERO-Plus上持续提升性能，在RoboTwin上的提升最高达6.26%。这些结果证明了度量几何在机器人操作中的有效性和可推广性。
英文摘要
Recent embodied models increasingly leverage geometric representations to improve spatial reasoning and robotic manipulation. However, existing reconstruction methods only reconstruct relative geometry with arbitrary scales, causing predicted object dimensions and spatial distances to vary across scenes, viewpoints, and input configurations. This inconsistency prevents geometric perception from being directly aligned with robotic actions defined on the real-world scale. To address this limitation, we propose Metric-Aware Geometry Perception (MAGP), an end-to-end, plug-and-play framework for metric geometry reconstruction that can be seamlessly integrated into robotic policies. At its core, Metric Scale Equivariant Augmentation encourages the model to reconstruct metric geometry from camera parameters and depth observations, ensuring that the reconstructed geometry follows the metric scale specified by observations. Flexible Metric Conditioning further enables MAGP to support arbitrary view counts and combinations of camera and depth inputs, improving robustness to heterogeneous robotic sensing configurations. Together, these designs produce geometrically consistent reconstructions with stable object dimensions and spatial distances across scenes and sensing conditions. Experiments on ETH3D, MegaDepth, and ScanNet++ demonstrate that MAGP maintains strong relative geometry accuracy while reducing the absolute error by over an order of magnitude, from 2.01m to 0.07m. When integrated into multiple robotic policies, MAGP consistently improves performance on LIBERO, RoboTwin, and zero-shot LIBERO-Plus, with gains of up to 6.26% on RoboTwin. These results demonstrate the effectiveness and generalizability of metric geometry for robotic manipulation.
发表机构
- Beihang University（北京航空航天大学）
- XiaoyuBot（小宇机器人）
机构由 AI 辅助整理，请以论文原文为准。
