通过主成分分析对点云进行姿态归一化，提升机器人控制的鲁棒性。
Robust Point Cloud Reinforcement Learning via PCA-Based Canonicalization
- 用PCA将任意视角点云映射到统一坐标系，消除视角差异
- 在多个复杂任务中显著提升对未见相机姿态的适应能力
- 适合需要稳定视觉输入的机器人控制场景
近年来，基于原始视觉输入的强化学习取得了显著进展，但对分布外变化（如光照、色彩、视角）仍显脆弱。点云强化学习（PC-RL）通过减少外观依赖性提供了有前景的替代方案，但在真实场景中仍受相机位姿不匹配的影响。为此，我们提出针对下游机器人控制的主成分点云（PPC）归一化框架，可将任意刚体变换下的点云映射至唯一规范姿态，使观测对齐到一致参考系，从而大幅降低视角引起的不一致性。实验表明，PPC在多个挑战性机器人任务中显著提升了对未见相机姿态的鲁棒性，为领域随机化提供了一种更合理的替代方案。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) from raw visual input has achieved impressive successes in recent years, yet it remains fragile to out-of-distribution variations such as changes in lighting, color, and viewpoint. Point Cloud Reinforcement Learning (PC-RL) offers a promising alternative by mitigating appearance-based brittleness, but its sensitivity to camera pose mismatches continues to undermine reliability in realistic settings. To address this challenge, we propose PCA Point Cloud (PPC), a canonicalization framework specifically tailored for downstream robotic control. PPC maps point clouds under arbitrary rigid-body transformations to a unique canonical pose, aligning observations to a consistent frame, thereby substantially decreasing viewpoint-induced inconsistencies. In our experiments, we show that PPC improves robustness to unseen camera poses across challenging robotic tasks, providing a principled alternative to domain randomization.
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