Belief Regulated Dual Propagation Nets for Learning Action Effects on Groups of Articulated Objects
Belief Regulated Dual Propagation Nets for Learning Action Effects on Groups of Articulated Objects
复制标题
用于学习对关节对象组的动作影响的信念调节双传播网络
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Emre Ugur
中科院分区：
文献类型：
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作者：
Ahmet E. Tekden;Aykut Erdem;Erkut Erdem;Mert Imre;M. Seker;Emre Ugur
近年来，图神经网络已成功应用于学习复杂且部分可观测物理系统的动力学。然而，到目前为止，它们在机器人领域的应用仍然有限。在本文中，我们介绍了信念调节的双传播网络（BRDPN），这是一种通用的可学习物理引擎，它使机器人能够预测其在包含多个关节式多部件物体组的场景中的动作效果。具体而言，我们的框架扩展了最近提出的传播网络（PropNets），并由两个互补的组件组成，一个物理预测器和一个信念调节器。前者预测机器人操作的物体的未来状态，而后者不断纠正机器人关于物体及其关系的知识。我们的结果表明，在模拟器中训练后，机器人能够在物体轨迹层面可靠地预测其动作的后果，并利用自身的交互经验来纠正其对环境状态的信念，从而在部分可观测环境中实现更好的预测。此外，经过训练的模型被迁移到现实世界中，并在预测最初未知关节关系的被推动交互物体的轨迹方面得到了验证。我们将BRDPN与PropNets进行了比较，结果表明BRDPN的性能一直很好。此外，由于关系可以在线预测，BRDPN能够调整其物理预测。
In recent years, graph neural networks have been successfully applied for learning the dynamics of complex and partially observable physical systems. However, their use in the robotics domain is, to date, still limited. In this paper, we introduce Belief Regulated Dual Propagation Networks (BRDPN), a general-purpose learnable physics engine, which enables a robot to predict the effects of its actions in scenes containing groups of articulated multi-part objects. Specifically, our framework extends recently proposed propagation networks (PropNets) and consists of two complementary components, a physics predictor and a belief regulator. While the former predicts the future states of the object(s) manipulated by the robot, the latter constantly corrects the robot’s knowledge regarding the objects and their relations. Our results showed that after training in a simulator, the robot can reliably predict the consequences of its actions in object trajectory level and exploit its own interaction experience to correct its belief about the state of the environment, enabling better predictions in partially observable environments. Furthermore, the trained model was transferred to the real world and verified in predicting trajectories of pushed interacting objects whose joint relations were initially unknown. We compared BRDPN against PropNets, and showed that BRDPN performs consistently well. Moreover, BRDPN can adapt its physic predictions, since the relations can be predicted online.
DOI：
10.1109/icra.2016.7487714
发表时间：
2016-05
期刊：
2016 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
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作者：
R. M. Martin;S. Höfer;O. Brock
通讯作者：
R. M. Martin;S. Höfer;O. Brock