Robust Point Cloud Reinforcement Learning via PCA-Based Canonicalization

2025-10-23

AI Summary

Point cloud-based reinforcement learning (PC-RL) suffers from poor cross-view policy generalization due to its sensitivity to camera pose, hindering real-world deployment. To address this, we propose a Principal Component Analysis (PCA)-based point cloud pose normalization framework—the first application of PCA to PC-RL observation preprocessing. By computing the principal axes of the point cloud and aligning them consistently with respect to orientation and origin, our method maps observations under arbitrary rigid transformations into a unique canonical coordinate system, thereby eliminating viewpoint-induced bias. Unlike conventional domain randomization, our approach requires no additional data augmentation or simulation configuration. Extensive evaluation across diverse robotic control tasks demonstrates significant improvements in robustness to unseen camera poses and policy generalization performance. The proposed method provides an interpretable, lightweight, and effective solution for achieving observation consistency in PC-RL, advancing its practical applicability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Machine Learning: Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Addresses point cloud RL sensitivity to camera pose mismatches
• Proposes canonicalization framework for consistent observation alignment
• Enhances robustness to unseen camera poses in robotics

Innovation

• PCA canonicalization aligns point clouds to consistent frame
• Maps arbitrary rigid-body transformations to unique canonical pose
• Reduces viewpoint inconsistencies for robust robotic control