论文标题
使用灵活的仿真环境的桥接场景理解和任务执行
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments
论文作者
论文摘要
在场景理解中取得了重大进展，该理解旨在建立世界的3D，指标和面向对象的表示。同时，强化学习使模拟的进步在很大程度上取得了令人印象深刻的进步。相比之下，对于感知算法的仿真关注较少。随着复杂的感知方法（例如公制的语义映射或3D动态场景图生成）需要精确的3D，2D和惯性信息，模拟变得越来越重要。为此，我们介绍了TESSE（具有语义分割环境的任务执行），这是一种开源模拟器，用于开发场景理解和任务执行算法。 Tesse已被用来开发用于公制的语义映射和3D动态场景图的最新解决方案。此外，Tesse在2020年国际机器人与自动化会议（ICRA）会议上担任了Goseek挑战的平台，这是一项对象搜索竞赛，重点是强调增强学习。 Tesse的代码可从https://github.com/mit-tesse获得。
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.
