TANGO：预测移动机械手工具交互的常识推广
TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators
摘要：
在工厂或家庭中帮助我们的机器人必须学会使用物体作为工具来执行任务，例如，一种用来装东西的托盘。我们考虑的问题，学习常识的知识，当一个工具可能是有用的，以及如何使用它可以组成与其他工具，以完成一个高层次的任务指示的人。我们介绍了一种新的神经模型，称为TANGO，用于预测特定于任务的工具交互，使用人类教师指导虚拟机器人的演示进行训练。TANGO使用图神经网络编码世界状态，包括对象和它们之间的符号关系。该模型使用目标和动作历史的知识学习参与场景，最终解码要执行的符号动作。至关重要的是，我们解决泛化到一些已知的工具丢失，但替代看不见的工具存在的看不见的环境。我们表明，通过使用来自知识库的预训练嵌入来增强环境的表示，该模型可以有效地推广到新的环境。实验结果表明，60.5-78.9%的绝对改善的基线预测成功的符号计划在看不见的设置模拟移动机械手。
英文摘要：
Robots assisting us in factories or homes must learn to make use of objects as tools to perform tasks, e.g., a tray for carrying objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot. TANGO encodes the world state, comprising objects and symbolic relationships between them, using a graph neural network. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but alternative unseen tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show a 60.5-78.9% absolute improvement over the baseline in predicting successful symbolic plans in unseen settings for a simulated mobile manipulator.
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。
