TL;DR
The paper introduces Interactive Gibson Benchmark, a comprehensive platform for evaluating robot navigation strategies that involve physical interaction with objects in cluttered indoor environments, combining high-fidelity simulation and new metrics.
Contribution
It presents the first benchmark with a new environment and metrics for studying interactive navigation, enabling evaluation of learning-based approaches in cluttered indoor scenes.
Findings
Multiple baseline methods evaluated, revealing trade-offs between path efficiency and object disturbance.
The benchmark is publicly available for community use.
Insights into navigation regimes with different interaction strategies.
Abstract
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…
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