The objective of Habitat Challenge is to benchmark the state-of-the-art in goal-directed visual navigation on the PointGoal task as defined by Anderson et al. [1]. To participate in the challenge follow the instructions at link. The challenge is built on top of the habitat stack. The winners of challenge will be announced at CVPR-2019 at the Habitat: Embodied Agents Challenge and Workshop. Details on the prizes coming soon.
The challenge is hosted on EvalAI, to participate please refer to the instructions. To get started:
- Install the Habitat-Sim and Habitat-API packages.
- Download the Gibson dataset following the instructions here. After downloading extract the dataset to folder
habitat-api/data/scene_datasets/gibson/folder (this folder should contain the.glbfiles from gibson). Note that thehabitat-apifolder is the habitat-api repository folder. - Download the dataset for Gibson pointnav from link and place it in the folder
habitat-api/data/datasets/pointnav/gibson. If placed correctly, you should have the train and val splits athabitat-api/data/datasets/pointnav/gibson/v1/train/andhabitat-api/data/datasets/pointnav/gibson/v1/val/respectively. - An example PPO baseline for the Pointnav task is present at
habitat-api/baselines. To start training on the Gibson dataset using this implementation follow instructions in the README. Set--task-configtotasks/pointnav_gibson.yamlto train on Gibson data. This is a good starting point for participants to start building their own models. The PPO implementation contains initialization and interaction with the environment as well as tracking of training statistics. Participants can borrow the basic blocks from this implementation to start building their own models. - To evaluate a trained PPO model on Gibson val split run
evaluate_ppo.pyusing instructions in the README athabitat-api/baselineswith--task-configset totasks/pointnav_gibson.yaml. The evaluation script will report SPL metric. - You can also use the general benchmarking script present at
benchmark.py. Set--task-configtotasks/pointnav_gibson.yamland inside the filetasks/pointnav_gibson.yamlset theSPLITtoval. - Instructions for submission will be made available soon when our challenge submission infrastructure goes live.
The objective of the agent is to navigate successfully to a target location specified by agent-relative Euclidean coordinates at the outset of an episode. As the agent steps around in the environment this goal vector gets updated according to the latest position of agent in environment. The action space for the agent consists of turn-left, turn-right, move forward and STOP actions. The turn actions produce a turn of 10 degrees and move forward action produces a linear displacement of 0.25m. The STOP action is used by the agent to indicate completion of an episode. We use an idealized embodied agent with a cylindrical body with diameter 0.2m and height 1.5m. The challenge consists of two tracks, RGB (only RGB input) and RGBD (RGB and Depth inputs).
We create a set of PointGoal navigation episodes for the Gibson [2] environment as the main dataset for the challenge. We use the splits provided by the Gibson dataset, retaining the training, validation and test sets.
After calling the STOP action, the agent is evaluated using the "Success weighted by Path Length" (SPL) metric [1].
An episode is deemed successful if on calling the STOP action, the agent is within 0.2m of the goal position. The evaluation will be carried out in completely new houses which are not present in training and validation splits.
Coming soon.
Habitat-Challenge is MIT licensed. See the LICENSE file for details.
[1] P. Anderson, A. X. Chang, D. S. Chaplot, A. Dosovitskiy, S. Gupta, V. Koltun, J. Kosecka, J. Malik, R. Mottaghi, M. Savva, and A. R. Zamir, "On evaluation of embodied navigation agents," arXiv:1807.06757, 2018
[2] F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese, "Gibson env: Real-world perception for embodied agents," in CVPR, 2018
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