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Habitat Challenge 2021

This repository contains the starter code for the 2021 challenge, details of the tasks, and training and evaluation setups. For an overview of habitat-challenge, visit

If you are looking for our 2020/2019 starter code, it’s available in the challenge-YEAR branch.

This year, we are hosting challenges on two embodied navigation tasks:

  1. PointNav (‘Go 5m north, 3m west relative to start’)
  2. ObjectNav (‘find a chair’).

Task #1: PointNav focuses on realism and sim2real predictivity (the ability to predict the performance of a nav-model on a real robot from its performance in simulation).

Task #2: ObjectNav focuses on egocentric object/scene recognition and a commonsense understanding of object semantics (where is a fireplace typically located in a house?).

New in 2021

The results of Habitat Challenge 2020 indicate that these benchmarks are far from being solved or stagnated. Thus, the task specifications remained unchanged except for the agent’s camera's tilt angle for the PointNav task. The agent can observe the area in front of it as the agent’s camera has now tilted.

We reserve the right to use additional metrics to choose winners in case of statistically insignificant SPL differences.

Task 1: PointNav

In PointNav, an agent is spawned at a random starting position and orientation in an unseen environment and asked to navigate to target coordinates specified relative to the agent’s start location (‘Go 5m north, 3m west relative to start’). No ground-truth map is available, and the agent must only use its sensory input (an RGB-D camera) to navigate.


We use Gibson 3D scenes for the challenge. As in the 2020 Habitat challenge, we use the Gibson dataset’s splits, retaining the train and val sets, and separating the test set into test-standard and test-challenge. The train and val scenes are provided to participants. The test scenes are used for the official challenge evaluation and are not provided to participants.


After calling the STOP action, the agent is evaluated using the ‘Success weighted by Path Length’ (SPL) metric [2].

An episode is deemed successful if on calling the STOP action, the agent is within 0.36m (2x agent-radius) of the goal position.

Task 2: ObjectNav

In ObjectNav, an agent is initialized at a random starting position and orientation in an unseen environment and asked to find an instance of an object category (‘find a chair’) by navigating to it. A map of the environment is not provided and the agent must only use its sensory input to navigate.

The agent is equipped with an RGB-D camera and a (noiseless) GPS+Compass sensor. GPS+Compass sensor provides the agent’s current location and orientation information relative to the start of the episode. We attempt to match the camera specification (field of view, resolution) in simulation to the Azure Kinect camera, but this task does not involve any injected sensing noise.


We use 90 of the Matterport3D scenes (MP3D) with the standard splits of train/val/test as prescribed by Anderson et al. [2]. MP3D contains 40 annotated categories. We hand-select a subset of 21 by excluding categories that are not visually well defined (like doorways or windows) and architectural elements (like walls, floors, and ceilings).


We generalize the PointNav evaluation protocol used by [1,2,3] to ObjectNav. At a high-level, we measure performance along the same two axes:

  • Success: Did the agent navigate to an instance of the goal object? (Notice: any instance, regardless of distance from starting location.)
  • Efficiency: How efficient was the agent’s path compared to an optimal path? (Notice: optimal path = shortest path from the agent’s starting position to the closest instance of the target object category.)

Concretely, an episode is deemed successful if on calling the STOP action, the agent is within 1.0m Euclidean distance from any instance of the target object category AND the object can be viewed by an oracle from that stopping position by turning the agent or looking up/down. Notice: we do NOT require the agent to be actually viewing the object at the stopping location, simply that such oracle-visibility is possible without moving. Why? Because we want participants to focus on navigation, not object framing. In Embodied AI’s larger goal, the agent is navigating to an object instance to interact with it (say point at or manipulate an object). Oracle-visibility is our proxy for ‘the agent is close enough to interact with the object’.

ObjectNav-SPL is defined analogous to PointNav-SPL. The only key difference is that the shortest path is computed to the object instance closest to the agent start location. Thus, if an agent spawns very close to ‘chair1’ but stops at a distant ‘chair2’, it will achieve 100% success (because it found a ‘chair’) but a fairly low SPL (because the agent path is much longer compared to the oracle path).

Participation Guidelines

Participate in the contest by registering on the EvalAI challenge page and creating a team. Participants will upload docker containers with their agents that are evaluated on an AWS GPU-enabled instance. Before pushing the submissions for remote evaluation, participants should test the submission docker locally to ensure it is working. Instructions for training, local evaluation, and online submission are provided below.

[NEW] For your convenience, please check our Habitat Challenge video tutorial and Colab step-by-step tutorial for this year.

Local Evaluation

  1. Clone the challenge repository:

    git clone
    cd habitat-challenge
  2. Implement your own agent or try one of ours. We provide an agent in that takes random actions:

    import habitat
    class RandomAgent(habitat.Agent):
        def reset(self):
        def act(self, observations):
            return {"action": numpy.random.choice(task_config.TASK.POSSIBLE_ACTIONS)}
    def main():
        agent = RandomAgent(task_config=config)
        challenge = habitat.Challenge()

    [Optional] Modify file if your agent needs any custom modifications (e.g. command-line arguments). Otherwise, nothing to do. Default is simply a call to RandomAgent agent in

  3. Install nvidia-docker v2 following instructions here: Note: only supports Linux; no Windows or MacOS.

  4. Modify the provided Dockerfile if you need custom modifications. Let’s say your code needs pytorch, these dependencies should be pip installed inside a conda environment called habitat that is shipped with our habitat-challenge docker, as shown below:

    FROM fairembodied/habitat-challenge:2021
    # install dependencies in the habitat conda environment
    RUN /bin/bash -c ". activate habitat; pip install torch"
    ADD /
    ADD /

    Build your docker container: docker build . --file Pointnav.Dockerfile -t pointnav_submission or using docker build . --file Objectnav.Dockerfile -t objectnav_submission. (Note: you may need sudo priviliges to run this command.)

  5. a) PointNav: Download Gibson scenes used for Habitat Challenge. Accept terms here and select the download corresponding to “Habitat Challenge Data for Gibson (1.5 GB)“. Place this data in: habitat-challenge/habitat-challenge-data/data/scene_datasets/gibson

    b) ObjectNav: Download Matterport3D scenes used for Habitat Challenge here. Place this data in: habitat-challenge/habitat-challenge-data/data/scene_datasets/mp3d

    Using Symlinks: If you used symlinks (i.e. ln -s) to link to an existing download of Gibson or MP3D, there is an additional step. For ObjectNav/MP3D (and similarly for PointNav/Gibson), first, make sure there is only one level of symlink (instead of a symlink to a symlink link to a .... symlink) with

    ln -f -s $(realpath habitat-challenge-data/data/scene_datasets/mp3d) \

    Then modify the docker command test_locally_objectnav_rgbd to mount the linked to location by adding -v $(realpath habitat-challenge-data/data/scene_datasets/mp3d). The modified docker command would be

     docker run \
         -v $(pwd)/habitat-challenge-data:/habitat-challenge-data \
         -v $(realpath habitat-challenge-data/data/scene_datasets/mp3d) \
         --runtime=nvidia \
         -e "AGENT_EVALUATION_TYPE=local" \
         -e "TRACK_CONFIG_FILE=/challenge_objectnav2021.local.rgbd.yaml" \
  6. Evaluate your docker container locally:

    # Testing PointNav
    ./ --docker-name pointnav_submission
    # Testing ObjectNav
    ./ --docker-name objectnav_submission

    If the above command runs successfully you will get an output similar to:

    2021-02-14 21:23:51,798 initializing sim Sim-v0
    2021-02-14 21:23:52,820 initializing task Nav-v0
    2021-02-14 21:23:56,339 distance_to_goal: 5.205519378185272
    2021-02-14 21:23:56,339 spl: 0.0

    Note: this same command will be run to evaluate your agent for the leaderboard. Please submit your docker for remote evaluation (below) only if it runs successfully on your local setup.

Online submission

Follow instructions in the submit tab of the EvalAI challenge page (coming soon) to submit your docker image. Note that you will need a version of EvalAI >= 1.2.3. Pasting those instructions here for convenience:

# Installing EvalAI Command Line Interface
pip install "evalai>=1.3.5"

# Set EvalAI account token
evalai set_token <your EvalAI participant token>

# Push docker image to EvalAI docker registry
# Pointnav
evalai push pointnav_submission:latest --phase <phase-name>

# Objectnav
evalai push objectnav_submission:latest --phase <phase-name>

The challenge consists of the following phases:

  1. Minival phase: This split is same as the one used in ./test_locally_{pointnav, objectnav} The purpose of this phase/split is sanity checking -- to confirm that our remote evaluation reports the same result as the one you’re seeing locally. Each team is allowed maximum of 100 submissions per day for this phase, but please use them judiciously. We will block and disqualify teams that spam our servers.
  2. Test Standard phase: The purpose of this phase/split is to serve as the public leaderboard establishing the state of the art; this is what should be used to report results in papers. Each team is allowed maximum of 10 submissions per day for this phase, but again, please use them judiciously. Don’t overfit to the test set.
  3. Test Challenge phase: This phase/split will be used to decide challenge winners. Each team is allowed a total of 5 submissions until the end of challenge submission phase. The highest performing of these 5 will be automatically chosen. Results on this split will not be made public until the announcement of final results at the Embodied AI workshop at CVPR.

Note: Your agent will be evaluated on 1000-2000 episodes and will have a total available time of 48 hours to finish. Your submissions will be evaluated on AWS EC2 p2.xlarge instance which has a Tesla K80 GPU (12 GB Memory), 4 CPU cores, and 61 GB RAM. If you need more time/resources for evaluation of your submission please get in touch. If you face any issues or have questions you can ask them by opening an issue on this repository.

PointNav/ObjectNav Baselines and DD-PPO Training Starter Code

We have added a config in configs/ddppo_pointnav.yaml | configs/ddppo_objectnav.yaml that includes a baseline using DD-PPO from Habitat-Lab.

  1. Install the Habitat-Sim and Habitat-Lab packages. Also ensure that habitat-baselines is installed when installing Habitat-Lab by installing it with python develop --all

  2. Download the Gibson dataset following the instructions here. After downloading extract the dataset to folder habitat-challenge/habitat-challenge-data/data/scene_datasets/gibson/ folder (this folder should contain the .glb files from gibson). Note that the habitat-lab folder is the habitat-lab repository folder. The data also needs to be in the habitat-challenge-data/ in this repository.

  3. Pointnav: Download the dataset for Gibson PointNav from link and place it in the folder habitat-challenge/habitat-challenge-data/data/datasets/pointnav/gibson. If placed correctly, you should have the train and val splits at habitat-challenge/habitat-challenge-data/data/datasets/pointnav/gibson/v2/train/ and habitat-challenge/habitat-challenge-data/data/datasets/pointnav/gibson/v2/val/ respectively. Place Gibson scenes downloaded in step-4 of local-evaluation under the habitat-challenge/habitat-challenge-data/data/scene_datasets folder. If you have already downloaded thes files for the habitat-lab repo, you may simply symlink them using ln -s $PATH_TO_SCENE_DATASETS habitat-challenge-data/data/scene_datasets (if on OSX or Linux).

    Objectnav: Download the episodes dataset for Matterport3D ObjectNav from link and place it in the folder habitat-challenge/habitat-challenge-data/data/datasets/objectnav/mp3d. If placed correctly, you should have the train and val splits at habitat-challenge/habitat-challenge-data/data/datasets/objectnav/mp3d/v1/train/ and habitat-challenge/habitat-challenge-data/data/datasets/objectnav/mp3d/v1/val/ respectively. Place Gibson scenes downloaded in step-4 of local-evaluation under the habitat-challenge/habitat-challenge-data/data/scene_datasets folder. If you have already downloaded thes files for the habitat-lab repo, you may simply symlink them using ln -s $PATH_TO_SCENE_DATASETS habitat-challenge-data/data/scene_datasets (if on OSX or Linux).

  4. An example on how to train DD-PPO model can be found in habitat-lab/habitat_baselines/rl/ddppo. See the corresponding README in habitat-lab for how to adjust the various hyperparameters, save locations, visual encoders and other features.

    1. To run on a single machine use the following script from habitat-lab directory, where $task={pointnav, objectnav}:
      export GLOG_minloglevel=2
      export MAGNUM_LOG=quiet
      set -x
      python -u -m torch.distributed.launch \
          --use_env \
          --nproc_per_node 1 \
          habitat_baselines/ \
          --exp-config configs/ddppo_${task}.yaml \
          --run-type train \
          TASK_CONFIG.DATASET.SPLIT 'train' 
    2. There is also an example of running the code distributed on a cluster with SLURM. While this is not necessary, if you have access to a cluster, it can significantly speed up training. To run on multiple machines in a SLURM cluster run the following script: change #SBATCH --nodes $NUM_OF_MACHINES to the number of machines and #SBATCH --ntasks-per-node $NUM_OF_GPUS and $SBATCH --gres $NUM_OF_GPUS to specify the number of GPUS to use per requested machine.
      #SBATCH --job-name=ddppo
      #SBATCH --output=logs.ddppo.out
      #SBATCH --error=logs.ddppo.err
      #SBATCH --gres gpu:1
      #SBATCH --nodes 1
      #SBATCH --cpus-per-task 10
      #SBATCH --ntasks-per-node 1
      #SBATCH --mem=60GB
      #SBATCH --time=12:00
      #SBATCH --signal=USR1@600
      #SBATCH --partition=dev
      export GLOG_minloglevel=2
      export MAGNUM_LOG=quiet
      export MASTER_ADDR=$(srun --ntasks=1 hostname 2>&1 | tail -n1)
      set -x
      srun python -u -m \
          --exp-config configs/ddppo_${task}.yaml \
          --run-type train \
          TASK_CONFIG.DATASET.SPLIT 'train' 
    3. Notes about performance: We have noticed that turning on the RGB/Depth sensor noise may lead to reduced simulation speed. As such, we recommend initially training with these noises turned off and using them for fine tuning if necessary. This can be done by commenting out the lines that include the key "NOISE_MODEL" in the config: habitat-challenge/configs/challenge_pointnav2021.local.rgbd.yaml.
    4. The preceding two scripts are based off ones found in the habitat_baselines/ddppo.
  5. The checkpoint specified by $PATH_TO_CHECKPOINT can evaluated by SPL and other measurements by running the following command:

    python -u -m \
        --exp-config configs/ddppo_${task}.yaml \
        --run-type eval \

    The weights used for our DD-PPO Pointnav or Objectnav baseline for the Habitat-2021 challenge can be downloaded with the following command:

    ```, where `$Task={pointnav, objectnav}.
  6. To submit your entry via EvalAI, you will need to build a docker file. We provide Dockerfiles ready to use with the DD-PPO baselines in ${Task}_DDPPO_baseline.Dockerfile, where $Task={Pointnav, Objectnav}. For the sake of completeness, we describe how you can make your own Dockerfile below. If you just want to test the baseline code, feel free to skip this bullet because ${Task}_DDPPO_baseline.Dockerfile is ready to use.

    1. You may want to modify the ${Task}_DDPPO_baseline.Dockerfile to include PyTorch or other libraries. To install pytorch, ifcfg and tensorboard, add the following command to the Docker file:

      RUN /bin/bash -c ". activate habitat; pip install ifcfg torch tensorboard"
    2. You change which and which script is used in the Docker, modify the following lines and replace the first or with your new files.:

    3. Do not forget to add any other files you may need in the Docker, for example, we add the demo.ckpt.pth file which is the saved weights from the DD-PPO example code.

    4. Finally modify the script to run the appropiate command to test your agents. The scaffold for this code can be found and the DD-PPO specific agent can be found in In this example, we only modify the final command of the PointNav/ObjectNav docker: by adding the following args to --model-path demo.ckpt.pth --input-type rgbd. The default script will pass these args to the python script. You may also replace the

      1. Please note that at this time, that habitat_baselines uses a slightly different config system and the configs nodes for habitat are defined under TASK_CONFIG which is loaded at runtime from BASE_TASK_CONFIG_PATH. We manually overwrite this config using the opts args in our file.
  7. Once your Dockerfile and other code is modified to your satisfcation, build it with the following command.

    docker build . --file ${Task}_DDPPO_baseline.Dockerfile -t ${task}_submission
  8. To test locally simple run the test_locally_${task} script. If the docker runs your code without errors, it should work on Eval-AI. The instructions for submitting the Docker to EvalAI are listed above.

  9. Happy hacking!

Citing Habitat Challenge 2021

Please cite the following paper for details about the 2021 PointNav challenge:

  title     =     {Sim2{R}eal {P}redictivity: {D}oes {E}valuation in {S}imulation {P}redict {R}eal-{W}orld {P}erformance?},
  author    =     {{Abhishek Kadian*} and {Joanne Truong*} and Aaron Gokaslan and Alexander Clegg and Erik Wijmans and Stefan Lee and Manolis Savva and Sonia Chernova and Dhruv Batra},
  journal   =   {IEEE Robotics and Automation Letters},
  year      =   {2020},
  volume    =   {5},
  number    =   {4},
  pages     =   {6670-6677},

Please cite the following paper for details about the 2021 ObjectNav challenge:

  title     =     {Object{N}av {R}evisited: {O}n {E}valuation of {E}mbodied {A}gents {N}avigating to {O}bjects},
  author    =     {Dhruv Batra and Aaron Gokaslan and Aniruddha Kembhavi and Oleksandr Maksymets and Roozbeh Mottaghi and Manolis Savva and Alexander Toshev and Erik Wijmans},
  booktitle =     {arXiv:2006.13171},
  year      =     {2020}


The Habitat challenge would not have been possible without the infrastructure and support of EvalAI team. We also thank the work behind Gibson and Matterport3D datasets.


[1] Habitat: A Platform for Embodied AI Research. Manolis Savva*, Abhishek Kadian*, Oleksandr Maksymets*, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, Dhruv Batra. IEEE/CVF International Conference on Computer Vision (ICCV), 2019.

[2] On evaluation of embodied navigation agents. Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir. arXiv:1807.06757, 2018.

[3] Are We Making Real Progress in Simulated Environments? Measuring the Sim2Real Gap in Embodied Visual Navigation. Abhishek Kadian*, Joanne Truong*, Aaron Gokaslan, Alexander Clegg, Erik Wijmans, Stefan Lee, Manolis Savva, Sonia Chernova, Dhruv Batra. arXiv:1912.06321, 2019.


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