In this quickstart we will briefly introduce the habitat stack using which we will setup the pointnav task and step around in the environment.


Habitat is a platform for embodied AI research that consists of:

  1. Habitat-Sim: A flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for MatterPort3D, Gibson and other datasets). [github-repo]
  2. Habitat Lab: A modular high-level library for end-to-end development in embodied AI — defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or no learning at all as in classical SLAM), and benchmarking their performance on the defined tasks using standard metrics. [github-repo]

For installing Habitat-Sim and Habitat Lab follow instructions here.


In this example we will setup a PointNav task in which the agent is tasked to go from a source location to a target location. For this example the agent will be you (the user). You will be able to step around in an environment using keys.

For running this example both Habitat-Sim and Habitat Lab should be installed successfully. The data for scene should also be downloaded (steps to do this are provided in the installation instructions of Habitat Lab). Running the code below also requires installation of cv2 which you can install using: pip install opencv-python.

import habitat
from habitat.sims.habitat_simulator.actions import HabitatSimActions
import cv2


def transform_rgb_bgr(image):
    return image[:, :, [2, 1, 0]]

def example():
    env = habitat.Env(

    print("Environment creation successful")
    observations = env.reset()
    print("Destination, distance: {:3f}, theta(radians): {:.2f}".format(
    cv2.imshow("RGB", transform_rgb_bgr(observations["rgb"]))

    print("Agent stepping around inside environment.")

    count_steps = 0
    while not env.episode_over:
        keystroke = cv2.waitKey(0)

        if keystroke == ord(FORWARD_KEY):
            action = HabitatSimActions.move_forward
            print("action: FORWARD")
        elif keystroke == ord(LEFT_KEY):
            action = HabitatSimActions.turn_left
            print("action: LEFT")
        elif keystroke == ord(RIGHT_KEY):
            action = HabitatSimActions.turn_right
            print("action: RIGHT")
        elif keystroke == ord(FINISH):
            action = HabitatSimActions.stop
            print("action: FINISH")
            print("INVALID KEY")

        observations = env.step(action)
        count_steps += 1

        print("Destination, distance: {:3f}, theta(radians): {:.2f}".format(
        cv2.imshow("RGB", transform_rgb_bgr(observations["rgb"]))

    print("Episode finished after {} steps.".format(count_steps))

    if (
        action == HabitatSimActions.stop
        and observations["pointgoal_with_gps_compass"][0] < 0.2
        print("you successfully navigated to destination point")
        print("your navigation was unsuccessful")

if __name__ == "__main__":

Running the above code will initialize an agent inside an environment, you can move around in the environment using W, A, D, F keys. On the terminal a destination vector in polar format will be printed with distance to goal and angle to goal. Once you are withing 0.2m of goal you can press the F key to stop and finish the episode successfully. If your finishing distance to goal is > 0.2m or if you spend more than 500 steps in the environment your episode will be unsuccessful.

Below is a demo of what the example output will look like:

For more examples refer to Habitat Lab examples and Habitat-Sim examples.


If you use habitat in your work, please cite:

  title =   {Habitat: A Platform for Embodied AI Research},
  author =  {Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh and Dhruv Batra},
  journal = {arXiv preprint arXiv:1904.01201},
  year =    {2019}