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udpate readme based on user feedback (facebookresearch#1860)
* udpate readme based on user feedback * addreee issues --------- Co-authored-by: Jimmy Yang <[email protected]>
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@@ -87,6 +87,11 @@ The observation of the social nav policy is defined under `habitat.gym.obs_keys` | |
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Note that if you want to add more or use other observation sensors, you can do that by adding sensors into `habitat.gym.obs_keys`. For example, you can provide a humanoid GPS to a policy's input by adding `agent_0_goal_to_agent_gps_compass` into `habitat.gym.obs_keys` in `hssd_spot_human_social_nav.yaml`. Notice that the observation key in `habitat.gym.obs_keys` must be a subset of sensors in `/habitat/task/lab_sensors`. Finally, another example would be adding an arm RGB sensor. You can do that by adding `agent_0_articulated_agent_arm_rgb` into `habitat.gym.obs_keys` in `hssd_spot_human_social_nav.yaml`. | ||
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For more advanced users, if you want to create a new sensor for social nav agents, there are three steps. | ||
- Step 1. Define a new sensor config class in `habitat.config.default_structured_configs.py`. | ||
- Step 2. Based on `type` string you define in `habitat.config.default_structured_configs.py`, create the same sensor name in sensor file using `@registry.register_sensor` method. See examples in `habitat.tasks.rearrange.social_nav.social_nav_sensors.py`. | ||
- Step 3. Register the new sensor in `hssd_spot_human_social_nav.yaml` for using it. It should be defined in `/habitat/task/lab_sensors` in config yaml, and in `habitat.gym.obs_keys` using `agent_0_{your_sensor_name}`. | ||
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### Action | ||
The action space of the social nav policy is defined under `/habitat/task/[email protected]_0_base_velocity: base_velocity_non_cylinder` in `habitat-lab/habitat/config/benchmark/multi_agent/hssd_spot_human_social_nav.yaml`. The action consists of linear and angular velocities. You can learn more about the hyperparameters for this action under `BaseVelocityNonCylinderActionConfig` in `habitat-lab/habitat/config/default_structured_configs.py`. | ||
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@@ -152,6 +157,7 @@ we have the following training wall clock time versus reward: | |
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We have the following training FPS: | ||
![Social Nav Training FPS](/res/img/habitat3_social_nav_training_fps.png) | ||
Note the training FPS depends on multiple factors such as the number of GPUs and the number of environments. | ||
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For evaluating the trained Spot robot's policy based on 500 episodes, run (please make sure `video_dir` and `eval_ckpt_path_dir` are the paths you want and the checkpoint is there): | ||
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Average episode num_agents_collide: 0.7020 | ||
``` | ||
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Note that in Habitat-3.0 paper, we report our numbers in the full evaluation dataset (1200 episodes). As a result, the number could be a bit different than the ones in the paper. | ||
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## Social Rearrangement | ||
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To run multi-agent training with a Spot robot and humanoid on the social rearrangement task. | ||
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