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Habitat-Baselines: Embodied AI baselines.

Project description



The habitat_baselines sub-package is NOT included upon installation by default. To install habitat_baselines, use the following command instead:

pip install -e habitat-lab
pip install -e habitat-baselines

This will also install additional requirements for each sub-module in habitat_baselines/, which are specified in requirements.txt files located in the sub-module directory.

Reinforcement Learning (RL)

Proximal Policy Optimization (PPO)


code: The PPO implementation is based on pytorch-a2c-ppo-acktr.

dependencies: A recent version of pytorch, for installing refer to

For training on sample data please follow steps in the repository README. You should download the sample test scene data, extract it under the main repo (habitat-lab/, extraction will create a data folder at habitat-lab/data) and run the below training command.


python -u -m \

You can reduce training time by changing the trainer from the default implement to VER by setting trainer_name to "ver" in either the config or via the command line.

python -u -m \
  --config-name=pointnav/ppo_pointnav_example.yaml \


python -u -m \
  --config-name=pointnav/ppo_pointnav_example.yaml \

We also provide trained RGB, RGBD, and Depth PPO models for MatterPort3D and Gibson. To use them download pre-trained pytorch models from link and unzip and specify model path here.

The habitat_baselines/config/pointnav/ppo_pointnav.yaml config has better hyperparameters for large scale training and loads the Gibson PointGoal Navigation Dataset instead of the test scenes. Change the /benchmark/nav/pointnav: pointnav_gibson in habitat_baselines/config/pointnav/ppo_pointnav.yaml to /benchmark/nav/pointnav: pointnav_mp3d in the defaults list for training on MatterPort3D PointGoal Navigation Dataset.

Hierarchical Reinforcement Learning (HRL)

We provide a two-layer hierarchical policy class, consisting of a low-level skill that moves the robot, and a high-level policy that reasons about which low-level skill to use in the current state. This can be especially powerful in long-horizon mobile manipulation tasks, like those introduced in Habitat2.0. Both the low- and high- level can be either learned or an oracle. For oracle high-level we use PDDL, and for oracle low-level we use instantaneous transitions, with the environment set to the final desired state. Additionally, for navigation, we provide an oracle navigation skill that uses A-star and the map of the environment to move the robot to its goal.

To run the following examples, you need the ReplicaCAD dataset.

To train a high-level policy, while using pre-learned low-level skills (SRL baseline from Habitat2.0), you can run:

python -u -m \

To run a rearrangement episode with oracle low-level skills and a fixed task planner, run:

python -u -m \
  --config-name=rearrange/rl_hierarchical.yaml \
  habitat_baselines.evaluate=True \
  habitat_baselines/rl/policy=hl_fixed \

To change the task (like set table) that you train your skills on, you can change the line /habitat/task/rearrange: rearrange_easy to /habitat/task/rearrange: set_table in the defaults of your config.

Habitat-3.0 Multi-Agent Training

First download the necessary data with python -m habitat_sim.utils.datasets_download --uids hssd-hab hab3-episodes habitat_humanoids hab3_bench_assets.

Social Navigation

To run multi-agent training with a Spot robot's policy being a low-level navigation policy and a humanoid's policy being a fixed (non-trainable) policy that navigates a sequence of navigation targets.

  • python habitat_baselines/ --config-name=social_nav/social_nav.yaml

For evaluating the trained Spot robot's policy

  • python habitat_baselines/ --config-name=social_nav/social_nav.yaml habitat_baselines.evaluate=True habitat_baselines.eval_ckpt_path_dir=/checkpoints/latest.pth habitat_baselines.eval.should_load_ckpt=True

Social Rearrangement

To run multi-agent training with a Spot robot and humanoid on the social rearrangement task.

  • Learn-Single: python habitat_baselines/ --config-name=social_rearrange/pop_play.yaml
  • Learn-Pop with 8 humanoid policies during training: python habitat_baselines/ --config-name=social_rearrange/pop_play.yaml habitat_baselines.rl.agent.num_pool_agents_per_type=[1,8]
  • Plan-Pop-4: python habitat_baselines/ --config-name=social_rearrange/plan_pop.yaml habitat_baselines.rl.policy.agent_1.hierarchical_policy.high_level_policy.plan_idx=4. To run Plan-Pop-p for other p values, set habitat_baselines.rl.policy.agent_1.hierarchical_policy.high_level_policy.plan_idx.

For zero-shot evaluate against the unseen agent population:

  • With planner-based collaborators: python habitat_baselines/ --config-name=social_rearrange/pop_play.yaml habitat_baselines.evaluate=True habitat_baselines.eval_ckpt_path_dir=PATH_TO_CKPT.pth +habitat_baselines.rl.policy.agent_1.hierarchical_policy.high_level_policy.select_random_goal=False +habitat_baselines.rl.policy.agent_1.hierarchical_policy.high_level_policy.plan_idx=1 change plan_idx to be 1, 2, 3, or 4 to evaluate against the other 4 planner agents.

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