发表机构
Tampere University（坦佩雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对机器人自主渐进式装配的感知需求，提出基于3D关键点的模块化学习框架PVRA，在装配位姿估计数据集上训练后，与以物体为中心的基线及增强指标对比验证了其有效性。
AI 中文摘要
现代计算机视觉已实现机器人装配操作的部分自主化。然而，自主执行渐进式装配操作除了感知物体外，还需要一套更具体的技能。通过对相关领域研究的对比分析，我们推断以物体为中心的感知必须向学习装配依赖关系推进，以预测自主装配操作所需的有意义可执行输出。随后，我们提出一种基于3D关键点的模块化学习框架，该框架可学习装配依赖关系，以在给定装配场景的RGB-D输入时推断可执行输出。我们在一个装配位姿估计数据集上训练并评估了所训练的网络，针对渐进式装配，我们使用一组增强的指标将其与以物体为中心的基线进行了比较。
英文摘要
Modern computer vision has enabled partial autonomy in robotic assembly manipulation. However, performing autonomous manipulation of a progressive assembly demands a more specific set of skills, in addition to perceiving the objects. Through a comparative analysis of research in the associated domains, we deduce that object-centric perception must advance towards learning assembly dependencies to predict meaningful actionable outputs for autonomous assembly manipulation. Subsequently, we present a 3D keypoint-based modular learning framework to learn assembly dependencies to infer actionable outputs given a RGB-D input of an assembly scene. We train and evaluate our trained network on an assembly pose estimation dataset and compare it against object-centric baselines with an augmented set of metrics for progressive assemblies.
Comments14 pages, 3 figures. Accepted for presentation at the European Conference on Robotics (ECoR) 2026
