We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task. For example, the robot can move objects if needed in order to clear a path leading to the goal location. Our benchmark comprises two novel elements: 1) a new experimental setup, the Interactive Gibson Environment (iGibson 0.5), which simulates high fidelity visuals of indoor scenes, and high fidelity physical dynamics of the robot and common objects found in these scenes; 2) a set of Interactive Navigation metrics which allows one to study the interplay between navigation and physical interaction. We present and evaluate multiple learning-based baselines in Interactive Gibson, and provide insights into regimes of navigation with different trade-offs between navigation path efficiency and disturbance of surrounding objects. We make our benchmark publicly available(https://sites.google.com/view/interactivegibsonenv) and encourage researchers from all disciplines in robotics (e.g. planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson.

翻译：我们提出了互动吉布森基准,这是培训和评估互动导航的第一个全面基准:机器人导航战略,允许与物体进行物理互动,甚至鼓励其完成任务。例如,机器人在必要时可以移动物体,以清除通往目标位置的道路。我们的基准包括两个新的要素:1)一个新的实验设置,即互动吉布森环境(iGibson 0.5),它模拟室内场景的高度忠诚视觉,以及机器人和在这些场景中发现的共同物体的高度忠诚物理动态;2)一套互动导航指标,它使人们可以研究导航与物理互动之间的相互作用。我们在互动吉布森中提出和评估多个基于学习的基线,并对导航系统提供洞见,说明导航效率与周围物体扰动之间的不同取舍。我们公布我们的基准(https://sites.google.com/view/interactivegibsonenv),并鼓励机器人各学科(例如规划、学习、控制)的研究人员在互动吉布利布利弗里提出、评价和比较其交互式导航解决方案。