Interactive Gibson Benchmark: A Benchmark For Interactive Navigation In Cluttered Environments

IEEE ROBOTICS AND AUTOMATION LETTERS(2020)

Cited 93|Views130
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Abstract
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot navigation problems where physical interaction with objects (e.g., pushing) is allowed and even encouraged to reach the goal. Our benchmark comprises two novel elements: 1) a new experimental simulated environment, the Interactive Gibson Environment, that generate photo-realistic images of indoor scenes and simulates realistic physical interactions of robots and common objects found in these scenes; 2) the Interactive Navigation Score, a novel metric to study the interplay between navigation and physical interaction of Interactive Navigation solutions. We present and evaluate multiple learning-based baselines in Interactive Gibson Benchmark, 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(1) and encourage researchers from related robotics disciplines (e.g., planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson Benchmark.
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Key words
Visual-based navigation, deep learning in robotics and automation, mobile manipulation
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