Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments
arXiv:1910.14442 · doi:10.1109/LRA.2020.2965078
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 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.
9 pages, 8 figures. Consider citing a newer version (https://arxiv.org/abs/2012.02924) if you are using iGibson
Cited by in corpus (8)
- Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence
- DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
- Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
- Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
- Learning Human Search Behavior from Egocentric Visual Inputs
- DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI
- FlightBench: Benchmarking Learning-based Methods for Ego-vision-based Quadrotors Navigation
- Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects