We present Interactive Gibson, 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, 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.

本文提出了交互式 Gibson benchmark，是首个用于训练和评估机器人导航策略的全面性基准测试。该基准测试由两个新实验元素组成，即交互式 Gibson 环境和一组交互式导航指标。我们在 Interactive Gibson 中提出并评估了多个基于学习的基准线，并提供了不同导航路径效率和干扰周围物体之间权衡的见解。我们公开了我们的基准测试，并鼓励机器人领域的所有研究者提出、评估和比较他们的交互式导航解决方案。

iGibson 0.5 交互导航在杂乱环境中的基准测试