# Configuration Tutorial All configuration files are stored in the `configs` folder and follow the standard json format. Each task contains its own configuration folder, which includes `action_config.json` and `gym_config.json`. For a more detailed guide on writing configurations to implement a task env, please refer to [EmbodiChain Data Generation](https://dexforce.github.io/EmbodiChain/main/tutorial/data_generation.html#). Only a brief introduction is provided here. --- ## 🔧 Gym Configuration ### 🎯 TaskSettings ```json { "id": "BeakerMixer-v0", "max_episodes": 100, "max_episode_steps": 500 } ``` | Field | Type | Required | Description | |----------------|------|----------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| | `id` | string | ✅ | The ID here must be exactly the same as the registered environment name, for example `@register_env("BeakerMixer-v0")` in the `RoboSynChallenge/robsynchallenge/tasks/beaker_mixer/beaker_mixer.py` | | `max_episodes` | int | ✅ | Number of **successful episodes** to collect. | | `max_episode_steps` | int | ✅ | Maximum step supported by the environment. | ### 🧠 Domain Randomization Configure task variation for better generalization. Most event functors can be found in detail in: 👉 [EmbodiChain Randomization Docs](https://dexforce.github.io/EmbodiChain/main/api_reference/embodichain/embodichain.lab.gym.envs.managers.html#module-embodichain.lab.gym.envs.managers.randomization). ```json { "random_light": { "func": "randomize_light", "mode": "interval", "interval_step": 10, "params": { "entity_cfg": {"uid": "light_0"}, "position_range": [[-0.5, -0.5, 2], [0.5, 0.5, 2]], "color_range": [[0.6, 0.6, 0.6], [1, 1, 1]], "intensity_range": [10.0, 30.0] } }, "random_camera_high_intrinsics": { "func": "randomize_camera_intrinsics", "mode": "reset", "params": { "entity_cfg": {"uid": "cam_high"}, "focal_x_range": [-50, 50], "focal_y_range": [-50, 50] } }, "random_camera_high_extrinsics": { "func": "randomize_camera_extrinsics", "mode": "reset", "params": { "entity_cfg": {"uid": "cam_high"}, "pos_range": [[0.0, -0.02, 0.0], [0.02, 0.02, 0.02]], "euler_range": [[-0.175, -0.175, -0.175], [0.175, 0.175, 0.175]] } }, "random_robot_init_eef_pose": { "func": "randomize_robot_eef_pose", "mode": "reset", "params": { "entity_cfg": {"uid": "CobotMagic", "control_parts": ["left_arm", "right_arm"]}, "position_range": [[-0.01, -0.01, -0.01], [0.01, 0.01, 0]] } }, "random_robot_qpos": { "func": "randomize_robot_qpos", "mode": "reset", "params": { "entity_cfg": {"uid": "CobotMagic"}, "qpos_range": [ [-0.06, -0.05, -0.06, -0.06, -0.05, -0.05, -0.06, -0.05, -0.06, -0.06, -0.05, -0.05], [0.06, 0.05, 0.06, 0.06, 0.05, 0.05, 0.06, 0.05, 0.06, 0.06, 0.05, 0.05] ], "relative_qpos": true, "joint_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] } }, "random_material": { "func": "randomize_visual_material", "mode": "interval", "interval_step": 10, "params": { "entity_cfg": {"uid": "table"}, "random_texture_prob": 0.5, "texture_path": "/root/workspace/RoboSynChallenge/assets/background_texture/100", "base_color_range": [[0.2, 0.2, 0.2], [1.0, 1.0, 1.0]] } }, "random_robot_material": { "func": "randomize_visual_material", "mode": "interval", "interval_step": 10, "params": { "entity_cfg": {"uid": "CobotMagic", "link_names": [".*"]}, "random_texture_prob": 0.5, "texture_path": "/root/workspace/RoboSynChallenge/assets/background_texture/100", "base_color_range": [[0.2, 0.2, 0.2], [1.0, 1.0, 1.0]] } }, "random_plane_material": { "func": "randomize_visual_material", "mode": "interval", "interval_step": 10, "params": { "entity_cfg": {"uid": "default_plane"}, "random_texture_prob": 0.5, "texture_path": "/root/workspace/RoboSynChallenge/assets/background_texture/100", "base_color_range": [[0.2, 0.2, 0.2], [1.0, 1.0, 1.0]] } }, "set_beaker_material": { "func": "set_rigid_object_visual_material", "mode": "startup", "params": { "entity_cfg": {"uid": "beaker"}, "mat_cfg": { "uid": "beaker_mat", "base_color": [1.0, 1.0, 0.999, 1.0], "ior": 1.47, "roughness": 0.005, "metallic": 0.0, "material_type": "BSDF" } } }, "random_beaker_mixer_material": { "func": "randomize_visual_material", "mode": "interval", "interval_step": 10, "params": { "entity_cfg": {"uid": "beaker_mixer"}, "random_texture_prob": 0.5, "texture_path": "/root/workspace/RoboSynChallenge/assets/background_texture/100", "base_color_range": [[0.2, 0.2, 0.2], [1.0, 1.0, 1.0]] } }, "random_beaker_mixer_pose": { "func": "randomize_rigid_object_pose", "mode": "reset", "params": { "entity_cfg": {"uid": "beaker_mixer"}, "position_range": [[0.54, 0.0, 0.9], [0.65, 0.1, 0.9]], "rotation_range": [[0, 0, 0], [0, 0, 0]], "relative_position": false, "relative_rotation": true } }, "random_beaker_pose": { "func": "randomize_rigid_object_pose", "mode": "reset", "params": { "entity_cfg": {"uid": "beaker"}, "position_range": [[0.63, -0.25 ,0.85], [0.75, -0.2, 0.85]], "rotation_range": [[0, 0, -180], [0, 0, 180]], "relative_position": false, "relative_rotation": true } }, "randomize_distractor_slots": { "func": "replace_distractor_slots_from_library", "mode": "reset", "params": { "entity_cfgs": [ {"uid": "distractor_0"}, {"uid": "distractor_1"} ], "library_index_path": "/root/workspace/RoboSynChallenge/robosynchallenge/Distractor/ASSET_INDEX.json", "categories": ["PaperCup", "fork", "spoon"], "position_ranges": [ [[0.4, -0.35, 0.89], [1.0, 0.35, 0.92]], [[0.4, -0.35, 0.89], [1.0, 0.35, 0.92]] ], "appear_probs": [1.0, 1.0], "z_rotation_ranges": [ [-180.0, 180.0], [-180.0, 180.0] ], "hide_position": [0.0, 0.0, -10.0], "avoid_uids": ["beaker", "beaker_mixer"], "min_distance_to_avoid": 0.4, "max_resample_attempts": 100, "physics_update_step": 1 } } } ``` ### 📷 Camera Configuration Camera configuration can be found in detail in: 👉 [EmbodiChain Camera Docs](https://dexforce.github.io/EmbodiChain/main/api_reference/embodichain/embodichain.lab.sim.sensors.html#embodichain.lab.sim.sensors.Camera). ```json { "sensor": [ { "sensor_type": "Camera", "uid": "cam_high", "width": 640, "height": 480, "enable_mask": false, "intrinsics": [606.315186, 606.100952, 320.549316, 245.877106], "extrinsics": { "eye": [ 0.257046, 0.049382, 1.459689 ], "target": [ 0.599275, 0.044517, 0.520085 ], "up": [ 0.939547, -0.010356, 0.342262 ] } }, { "sensor_type": "Camera", "uid": "cam_right_wrist", "width": 640, "height": 480, "enable_mask": false, "intrinsics": [603.117004394531, 602.418640136719, 326.727478027344, 251.933990478516], "extrinsics": { "parent": "right_link6", "pos": [-0.12, 0.01, 0.03], "quat": [0.1949, 0.6797, -0.6797, -0.1949] } }, { "sensor_type": "Camera", "uid": "cam_left_wrist", "width": 640, "height": 480, "enable_mask": false, "intrinsics": [603.117004394531, 602.418640136719, 326.727478027344, 251.933990478516], "extrinsics": { "parent": "left_link6", "pos": [-0.12, 0.01, 0.03], "quat": [0.1949, 0.6797, -0.6797, -0.1949] } } ] } ``` ### 📦 Data Collection Settings Dataset configuration can be found in detail in: 👉 [EmbodiChain Data Recorder Docs](https://dexforce.github.io/EmbodiChain/main/overview/gym/dataset_functors.html#lerobotrecorder). ```json { "dataset": { "lerobot": { "func": "LeRobotRecorder", "mode": "save", "params": { "save_path": "/root/workspace/RoboSynChallenge/lerobot_dataset/beaker_mixer_dual", "robot_meta": { "robot_type": "CobotMagic", "control_freq": 25, "control_parts": ["left_arm", "left_eef", "right_arm", "right_eef"] }, "instruction": { "lang": "Pick the beaker, place it on the mixer, then flip the toggle switch with the other arm." }, "extra": { "scene_type": "Sim", "task_description": "beaker_mixer_dual", "data_type": "sim" }, "use_videos": true } } } } ``` ## 🔧 Action Configuration All action logic is stored in `action_config.json`. This file adopts Directed Graph (DiGraph) to describe the complete dual-arm robot task workflow, covering action space definition, key pose generation, trajectory planning, multi-component timing constraints and debug switches. There are 5 top-level core fields in the config file: - `scope`: Define control modules, action dimensions and initialization rules for arms and grippers - `node`: Graph nodes, generate critical poses including initial joint status, pre-grasp pose, target grasp pose and IK solved joint angles - `edge`: Directed edges, plan smooth motion trajectories between two nodes with adjustable motion steps - `sync`: Synchronization dependency rules to control execution order between robot arms and grippers - `misc`: Auxiliary switches for visualization and trajectory smoothing --- ## 1. Scope Definition Four independent control modules are declared: right arm, left arm, left end-effector, right end-effector. Each module defines action dimension and initialization strategy. ```json { "scope": { "right_arm": { "type": "DiGraph", "dim": [6], "init": { "method": "current_qpos", "init_node_name": "right_arm_init_qpos" }, "dtype": "float32" }, "left_arm": {}, "left_eef": {}, "right_eef": {} } } ``` | Field | Type | Description | |-------|------|-------------| | `type` | string | Fixed as `DiGraph`, all motion modules use directed graph structure | | `dim` | list[int] | Action dimension: 6 DoF for each robotic arm, 1 dimension for gripper open/close control | | `init.method` | string | `current_qpos`: read real-time joint angles as initial state; `given_qpos`: use fixed preset value | | `dtype` | string | Uniformly set to `float32` for all pose and joint calculations | The left arm follows the same structure as the right arm. For grippers, set `dim: [1]` and use `given_qpos: [1]` to keep gripper open by default. Other modules can be configured by analogy. ## 2. Graph Node Definition Each node stands for a key task pose, such as home pose, pre-grasp pose, grasp target pose and placement pose. The most commonly used node converts Cartesian pose to joint angles via inverse kinematics. ```json { "node": { "right_arm": [ { "bottle_grasp_qpos": { "name": "generate_affordances_from_src", "kwargs": { "affordance_infos": [ { "src_key": "bottle_grasp_pose", "dst_key": "bottle_grasp_qpos", "valid_funcs_name_kwargs_proc": [ { "name": "get_ik_ret", "kwargs": { "ik_func": "env.robot.compute_ik", "qpos_seed": "env.affordance_datas['bottle_pre1_qpos']", "control_part": "right_arm" } } ] } ] } } } ] } } ``` | Field | Description | |-------|-------------| | `src_key` | Input parameter: Cartesian pose of target object | | `dst_key` | Output parameter: solved joint angles of robotic arm | | `get_ik_ret` | Core inverse kinematics solver, convert 6D Cartesian pose to joint qpos | Pre-grasp waypoints, placement waypoints and return home waypoints reuse this node template. Only modify input `src_key` and offset parameters for different tasks. ## 3. Directed Edge for Motion Trajectory Edges represent continuous robot movement between two nodes, including start node, end node and total simulation steps to adjust motion speed. ```json { "edge": { "right_arm": [ { "pre1_to_grasp": { "src": "bottle_pre1_qpos", "sink": "bottle_grasp_qpos", "duration": 24, "name": "plan_trajectory", "kwargs": { "agent_uid": "right_arm", "keypose_names": ["bottle_pre1_qpos", "bottle_grasp_qpos"] } } } ] } } ``` | Field | Description | |-------|-------------| | `src` / `sink` | Source start node and target sink node | | `duration` | Total simulation steps of this motion; larger value leads to slower movement | | `plan_trajectory` | Fixed interpolation function to generate smooth joint trajectories | All arm movements including lifting, pouring, placing and homing share this edge structure, just replace node names and adjust `duration`. ## 4. Synchronization Timing Rules The `sync` field defines execution dependencies to avoid disordered parallel motions. A typical scenario: the gripper closes only after the robotic arm reaches the grasp pose. ``` json { "sync": { "rclose0": { "depend_tasks": ["pre1_to_grasp"] }, "grasp_to_up": { "depend_tasks": ["rclose0"] } } } ``` | Field | Description | |-------|-------------| | `depend_tasks` | List of motion edges that must complete before running current edge | Add similar synchronization constraints for gripper release after placement and arm homing after object release by following the same logic. ## 5. Misc Auxiliary Configurations Global switches for debugging visualization and trajectory processing. Turn off all visualization options during dataset collection to reduce computation overhead. ```json { "misc": { "vis_graph": false, "vis_gantt": false, "warpping": true } } ``` | Field | Description | |-------|-------------| | `vis_graph` | Switch to render directed action graph for debugging | | `vis_gantt` | Switch to generate Gantt chart to inspect action timeline | | `warpping` | Trajectory smoothing module, must remain enabled | More details can be found in: 👉 [EmbodiChain Action Config Docs](https://dexforce.github.io/EmbodiChain/main/tutorial/data_generation.html#step-2-prepare-the-action-configuration)