File size: 2,050 Bytes
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"feature_type": "latent_student_spatial_kv",
"conditioning_variant": "b3",
"config": "configs/b2_rdt1b_lora.yaml",
"root": "dataset/datasets",
"splits": [
"train",
"validation",
"test"
],
"datasets": [
"libero_spatial"
],
"seed": 42,
"stage": null,
"normalize_actions": false,
"action_target_mode": "delta",
"state_dim": 9,
"action_dim": 7,
"proprioception_schema": "libero_joint7_gripper2_norm01_action7_v1",
"state_encoder_layout": "libero_ortho6d",
"action_encoder_layout": "libero_ortho6d",
"gripper_processing": "two finger qpos normalized independently with shared travel min/max to [0,1]; raw HDF5 action command",
"max_samples_per_episode": null,
"all_samples_per_episode": true,
"gripper_window_before": 3,
"gripper_window_after": 3,
"gripper_change_scope": "directional",
"open_to_close_before": 10,
"open_to_close_after": 11,
"close_to_open_before": 10,
"close_to_open_after": 11,
"student_model_id": "/workspace/model/Latent-Student-Spatial-Forcing-fixed/",
"processor_id": "/workspace/model/stage1_unsloth",
"spatial_parameters_path": null,
"latent_count": 6,
"spatial_token_count": 5,
"layer_index": 7,
"prompt_template": "You are a robot manipulation assistant. Given an observation image and a task instruction, predict the end-effector's 2D trajectory as 5 waypoints. Output ONLY the coordinate list in this exact format: [[x1,y1],[x2,y2],[x3,y3],[x4,y4],[x5,y5]]\n\nTask: The task is {task}. What is the trajectory that the end effector should take?",
"qwen_kv_dim": 2048,
"qwen_hidden_state_dim": 2560,
"waypoint_dim": 2,
"has_qwen_hidden_states": true,
"has_latent_waypoints": true,
"include_t5": true,
"t5_precision": "bf16",
"t5_batch_size": 32,
"cache_image_slots": true,
"image_history_size": 2,
"max_images_per_sample": 6,
"image_storage_codec": "raw_uint8",
"image_storage_lossless": true,
"image_storage_compressed": false,
"image_jpeg_quality": null,
"cache_layout": "sample_shard",
"batch_size": 32
}
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