Configuration Tutorial¶

All configuration files are stored in the task_config folder and follow the standard YAML format.

You can run bash task_config/create_task_config.sh ${task_config_name} to create new task configuration.

1. ✅ Minimal Example¶

Below is a minimal configuration file to start a typical data collection session:

render_freq: 0
episode_num: 100
use_seed: false
save_freq: 15
embodiment:
  - aloha-agilex
language_num: 100

domain_randomization:
  random_background: true
  cluttered_table: true
  clean_background_rate: 0.02
  random_head_camera_dis: 0
  random_table_height: 0.03
  random_light: true
  crazy_random_light_rate: 0.02
  random_embodiment: false

camera:
  head_camera_type: D435
  wrist_camera_type: D435
  collect_head_camera: true
  collect_wrist_camera: true

data_type:
  rgb: true
  third_view: false
  depth: false
  pointcloud: false
  observer: false
  endpose: false
  qpos: true
  mesh_segmentation: false
  actor_segmentation: false

pcd_down_sample_num: 1024
pcd_crop: true
dual_arm: true
save_path: ./data
clear_cache_freq: 5
collect_data: true
eval_video_log: true

2. 🔧 Configuration Breakdown¶

2.1 🎯 Task & Embodiment Settings¶

Field	Type	Required	Description
`embodiment`	list	✅	List of robot embodiment(s). For a dual-arm robot, use `[name]`, e.g., `[aloha-agilex]`; to combine two single-arm robots, use `[left, right, interval]`, e.g., `[piper, franka-panda, 0.6]`. The `interval` specifies the distance between arms (typically 0.6–0.8 meters).
`dual_arm`	bool	optional	Whether to use both arms. Default: `true`.
`use_seed`	bool	✅	Whether to use a predefined seed list from `task_config/seeds/`. If `false`, the system will automatically explore viable seeds.
`episode_num`	int	✅	Number of successful episodes to collect.
`language_num`	int	optional	If using language-conditioned task planning, sets the number of language descriptions to sample for each task.

2.2 🧠 Domain Randomization¶

Configure task variation for better generalization.

domain_randomization:
  random_background: true
  cluttered_table: true
  clean_background_rate: 0.02
  random_head_camera_dis: 0
  random_table_height: 0.03
  random_light: true
  crazy_random_light_rate: 0.02
  random_embodiment: false

Field	Type	Description
`random_background`	bool	Enable random textures for the table and background.
`cluttered_table`	bool	Add distractor objects to the table to simulate a cluttered environment.
`clean_background_rate`	float	Ratio of clean backgrounds (e.g., `0.02` = 2%). Only effective if `random_background` is `true`.
`random_head_camera_dis`	float	Random displacement applied to the head camera position (in meters).
`random_table_height`	float	Random variation in the table height (in meters).
`random_light`	bool	Enable randomized lighting during simulation.
`crazy_random_light_rate`	float	Probability of applying extreme lighting. Only effective if `random_light` is `true`.
`random_embodiment`	bool	Enable embodiment randomization (experimental, currently not fully supported).

2.3 📷 Camera Configuration¶

camera:
  head_camera_type: D435
  wrist_camera_type: D435
  collect_head_camera: true
  collect_wrist_camera: true

Field	Type	Description
`head_camera_type`	str	Camera used for global observation. Options: see `task_config/_camera_config.yml`.
`wrist_camera_type`	str	Camera used for close-up view.
`collect_head_camera`	bool	Whether to collect head-view data.
`collect_wrist_camera`	bool	Whether to collect wrist-view data.

2.4 📦 Data Collection Settings¶

Field	Type	Required	Description
`collect_data`	bool	✅	Enable actual data saving.
`save_freq`	int	✅	Save every N steps.
`save_path`	str	optional	Directory to save data. Default: `./data`.
`clear_cache_freq`	int	optional	Clear cached data every N episodes. Prevents memory overflow.
`eval_video_log`	bool	optional	Save evaluation videos for replay.

2.5 💾 Data Type¶

Specify which data to collect in each episode:

data_type:
  rgb: true
  third_view: false
  depth: false
  pointcloud: false
  observer: false
  endpose: false
  qpos: true
  mesh_segmentation: false
  actor_segmentation: false

Type	Description
`rgb`	RGB image from multiple views.
`third_view`	Third-person video.
`depth`	Depth images from cameras.
`pointcloud`	Merged point cloud of the scene.
`observer`	Observer-view RGB frame.
`endpose`	End-effector 6D pose.
`qpos`	Robot joint angles.
`mesh_segmentation`	Per-object segmentation from mesh.
`actor_segmentation`	Per-actor segmentation from RGB.

2.6 🔍 Point Cloud Settings¶

Field	Type	Description
`pcd_down_sample_num`	int	FPS (Farthest Point Sampling) number; set `0` to keep all points.
`pcd_crop`	bool	Whether to crop out table/walls based on known transforms.

2.7 🎥 Rendering¶

Field	Type	Description
`render_freq`	int	Render visualization every N steps. Set to `0` to disable. For servers without display, recommend `0`.

3. 📌 Notes¶

All task names must correspond to files in env/<task_name>.py.
For available embodiments and cameras, refer to:
task_config/_embodiment_config.yml
task_config/_camera_config.yml
The system supports both dual-arm and single-arm setups.
Seeds, if used, are located in task_config/seeds/.