论文标题
重排：体现AI的挑战
Rearrangement: A Challenge for Embodied AI
论文作者
论文摘要
我们描述了一个体现AI的研究和评估框架。我们的建议基于一项规范任务：重排。标准任务可以集中精力开发新技术，并作为可以转移到其他设置的训练有素的模型的来源。在重排任务中，目标是将给定的物理环境带入指定状态。目标状态可以通过对象姿势，图像，语言中的描述或让代理在目标状态中体验环境来指定目标状态。我们表征了沿不同轴的重排场景，并描述了基准重排性能的指标。为了促进研究和探索，我们在四个不同的模拟环境中介绍了重排方案的实验测试床。我们预计将发布其他数据集，并将构建新的仿真平台，以支持对重排代理及其在物理系统上的部署的培训。
We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techniques and serve as a source of trained models that can be transferred to other settings. In the rearrangement task, the goal is to bring a given physical environment into a specified state. The goal state can be specified by object poses, by images, by a description in language, or by letting the agent experience the environment in the goal state. We characterize rearrangement scenarios along different axes and describe metrics for benchmarking rearrangement performance. To facilitate research and exploration, we present experimental testbeds of rearrangement scenarios in four different simulation environments. We anticipate that other datasets will be released and new simulation platforms will be built to support training of rearrangement agents and their deployment on physical systems.
