提出点级关键点投票框架，让机器人学会判断装配顺序。
PVRA: A Pointwise Key-point Voting Framework for Robotic Assembly
- 用3D关键点投票机制建模装配依赖关系
- 在RGB-D输入下准确预测装配动作序列
- 适合需要理解装配逻辑的机器人任务
现代计算机视觉已实现机器人装配操作的部分自动化。然而，完成渐进式装配任务还需更特定的能力，除感知物体外，还需理解装配间的依赖关系。通过分析相关领域研究，我们发现以物体为中心的感知需向学习装配依赖关系演进，以预测有意义的可执行输出。为此，我们提出一种基于3D关键点的模块化学习框架，从RGB-D输入中学习装配依赖关系，推断出可执行的动作输出。我们在一个装配位姿估计数据集上训练并评估该网络，并与基于物体中心的基线方法进行对比，采用扩展的评估指标针对渐进式装配任务进行验证。
原文摘要 · Abstract (English)
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.
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