拆解姿态估计失败原因，精准修复提升机器人抓取稳定性
Tracing Back Error Sources to Explain and Mitigate Pose Estimation Failures
- 模块化设计分离错误检测、归因与修复，按需干预
- 相比基础模型，用更轻量的ICP实现相当性能
- 适合对实时性与鲁棒性要求高的真实机器人场景
在机器人操作中，稳健的姿态估计通常依赖于通用基础估计算法，这些算法试图在单一模型中处理多种误差源，但受限于环境不确定性，且推理时间长、计算开销大。本文提出一种模块化、不确定性感知的框架，能够将姿态估计误差归因于具体误差来源，并仅在必要时应用针对性缓解策略。以迭代最近点（ICP）作为轻量级姿态估计算法实例化，该框架在真实机器人抓取任务中显著提升了ICP的鲁棒性，性能媲美基础模型，同时依赖更简单、更快的估计器。
原文摘要 · Abstract (English)
Robust estimation of object poses in robotic manipulation is often addressed using foundational general estimators, that aim to handle diverse error sources naively within a single model. Still, they struggle due to environmental uncertainties, while requiring long inference times and heavy computation. In contrast, we propose a modular, uncertainty-aware framework that attributes pose estimation errors to specific error sources and applies targeted mitigation strategies only when necessary. Instantiated with Iterative Closest Point (ICP) as a simple and lightweight pose estimator, we leverage our framework for real-world robotic grasping tasks. By decomposing pose estimation into failure detection, error attribution, and targeted recovery, we significantly improve the robustness of ICP and achieve competitive performance compared to foundation models, while relying on a substantially simpler and faster pose estimator.
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