KINet：用于机器人推动操作的无监督前向模型
KINet: Unsupervised Forward Models for Robotic Pushing Manipulation
AI总结：
本文提出无监督关键点交互网络KINet，利用视觉观测学习对象关键点图表示和动作条件前向模型，实现机器人推动操作中的可规划对象中心表示与准确前向预测。
摘要：
以对象为中心的表示是前向预测的一种基本抽象。现有的大多数前向模型通过大量监督（例如对象类别和边界框）来学习这种表示，但这类真值信息在现实中并不容易获得。为了解决这一问题，我们提出了 KINet（Keypoint Interaction Network，关键点交互网络）——一个端到端的无监督框架，用于基于关键点表示推理对象交互。利用视觉观测，我们的模型学习将对象与关键点坐标关联起来，并发现系统的图表示，即一组关键点嵌入及其关系。随后，它使用对比估计学习一个以动作为条件的前向模型，以预测未来的关键点状态。通过在关键点空间中学习执行物理推理，我们的模型能够自动泛化到具有不同对象数量、新颖背景和未见对象几何形状的场景。实验证明了我们的模型在准确执行前向预测以及为下游机器人推动操作任务学习可规划的对象中心表示方面的有效性。
英文摘要：
Object-centric representation is an essential abstraction for forward prediction. Most existing forward models learn this representation through extensive supervision (e.g., object class and bounding box) although such ground-truth information is not readily accessible in reality. To address this, we introduce KINet (Keypoint Interaction Network) -- an end-to-end unsupervised framework to reason about object interactions based on a keypoint representation. Using visual observations, our model learns to associate objects with keypoint coordinates and discovers a graph representation of the system as a set of keypoint embeddings and their relations. It then learns an action-conditioned forward model using contrastive estimation to predict future keypoint states. By learning to perform physical reasoning in the keypoint space, our model automatically generalizes to scenarios with a different number of objects, novel backgrounds, and unseen object geometries. Experiments demonstrate the effectiveness of our model in accurately performing forward prediction and learning plannable object-centric representations for downstream robotic pushing manipulation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
