用两阶段模型检测可开部件，提升机器人操作泛化能力
Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors
- 第一阶段用感知分组特征增强定位精度
- 第二阶段结合几何先验预测开合参数，准确率更高
- 适用于未见过的物体和场景，适合机器人抓取任务
检测可开部件对智能机器人执行如拉开抽屉等任务至关重要。该任务因需理解物体类别与运动特性而具多任务挑战性。现有方法多为特定类别或特定数据集训练，难以泛化至未见环境与物体。本文提出基于Transformer的可开部件检测框架MOPD，融合感知分组与几何先验，在性能上超越现有方法。第一阶段引入感知分组特征模型，通过交叉注意力机制提供感知分组先验，提升检测效果；第二阶段采用几何理解特征模型，提供几何特征先验以预测运动参数。相比现有方法，本方案在检测与运动参数预测上均表现更优。代码与模型已公开于https://github.com/lisiqi-zju/MOPD。
原文摘要 · Abstract (English)
Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://github.com/lisiqi-zju/MOPD
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