Interactive Gibson: A Benchmark for Interactive Navigation in Cluttered Environments

-- 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 tradeoffs between navigation path efficiency and disturbance of surrounding objects. Classical robot navigation is concerned with reaching goals while avoiding collisions [1], [2]. This definition of navigation is motivated by a wide variety of robot applications in factories or outdoor settings. As robots are increasingly deployed in complex and cluttered environments, physical interactions while navigating become not only unavoidable, but necessary. For example, when operating a robot in a cluttered home, the robot might need to push objects aside or open doors in order to be able to reach its destination. This problem is referred to as Interactive Navigation and in this paper we propose a principled and systematic way to study it (see Figure 1). The "aversion to interaction" in robot mobile agents is easy to understand: real robots are expensive, and interacting with the environment presents safety risks. In Robotic Manipulation these challenges have been addressed by extensive use of physics simulation engines [3], [4], [5], which simulate object and robot dynamics with high precision and thus allow one to study manipulation in a safe manner. Further, these engines can be used to train models which are deployable in the real world.