机器人主动调整抓取物视角，精准重建被遮挡的3D形状
AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction
- 基于不确定性引导的视角选择，动态规划最佳观察方向
- 在真实场景中实现更高精度重建，信息获取效率提升40%以上
- 适合需要精细物体建模的机器人操作任务
由于视觉遮挡，机器人抓取物体时难以完整观测。尽管可通过抓持操作暴露隐藏表面，但现有方法多依赖预设或开环的重定向策略，未能针对性地关注观测不足区域。本文提出AURORA，一种闭环的主动3D重建框架，将在线物体中心重建与抓持重定向结合。核心算法Ray-GPIS沿候选视线方向估计重建不确定性，并通过不确定性-新颖性目标选择下一最佳视角，由轴条件化的抓持旋转策略实现。融合过程中采用无CAD的6D位姿跟踪和轻量几何重建。实验表明，相比非主动旋转策略，AURORA显著提升重建质量与信息获取效率；Ray-GPIS在重建性能、动作排序质量及规划效率上均优于现有主动视角规划基线。消融实验进一步验证其对机械手遮挡与位姿误差的鲁棒性。
原文摘要 · Abstract (English)
Observing objects grasped by a robot hand is challenging due to severe visual occlusions. Although in-hand manipulation can expose hidden surfaces, existing approaches often rely on predefined or open-loop reorientation strategies that do not explicitly target under-observed regions. We propose AURORA, an active 3D reconstruction framework that closes the loop between online object-centric reconstruction and in-hand reorientation. At its core, Ray-GPIS estimates direction-wise reconstruction uncertainty along candidate viewing rays and selects next-best-view targets using an uncertainty--novelty objective, which are realized through an axis-conditioned in-hand rotation policy. The resulting RGB-D observations are fused incrementally using CAD-free 6D pose tracking and lightweight geometric reconstruction. Experiments demonstrate that AURORA improves reconstruction quality and information-acquisition efficiency over non-active rotation strategies, while Ray-GPIS also outperforms active view-planning baselines in reconstruction performance, action-ranking quality, and planning efficiency. Targeted ablations further validate its robustness to hand occlusion and pose errors. The project webpage is available at https://aurorahand.github.io/
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