Significant progress has been made in scene understanding which seeks to build 3D, metric and object-oriented representations of the world. Concurrently, reinforcement learning has made impressive strides largely enabled by advances in simulation. Comparatively, there has been less focus in simulation for perception algorithms. Simulation is becoming increasingly vital as sophisticated perception approaches such as metric-semantic mapping or 3D dynamic scene graph generation require precise 3D, 2D, and inertial information in an interactive environment. To that end, we present TESSE (Task Execution with Semantic Segmentation Environments), an open source simulator for developing scene understanding and task execution algorithms. TESSE has been used to develop state-of-the-art solutions for metric-semantic mapping and 3D dynamic scene graph generation. Additionally, TESSE served as the platform for the GOSEEK Challenge at the International Conference of Robotics and Automation (ICRA) 2020, an object search competition with an emphasis on reinforcement learning. Code for TESSE is available at https://github.com/MIT-TESSE.

中文翻译：

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在灵活的仿真环境中架起场景理解和执行任务的桥梁

场景理解已经取得了重大进展，它试图建立3D，度量和面向对象的世界表示。同时，强化学习取得了令人瞩目的进步，这在很大程度上得益于模拟技术的进步。相比之下，人们对感知算法的仿真关注较少。随着复杂的感知方法（例如度量语义映射或3D动态场景图生成）在交互式环境中需要精确的3D，2D和惯性信息，仿真变得越来越重要。为此，我们介绍了TESSE（带有语义分割环境的任务执行），这是一个开放源代码的模拟器，用于开发场景理解和任务执行算法。TESSE已被用于开发用于度量语义映射和3D动态场景图生成的最新解决方案。此外，TESSE还作为2020年国际机器人与自动化大会（ICRA）的GOSEEK挑战赛的平台，这是一个以强化学习为重点的对象搜索竞赛。TESSE的代码可从https://github.com/MIT-TESSE获得。