File size: 2,760 Bytes
4c95857 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # PointmapVLA checkpoint configuration (release schema).
#
# framework.* model architecture; framework.qwenvl.base_vlm is resolved relative to this
# directory (base_vlm/ holds the Qwen3.5 config, tokenizer and processor only;
# every weight comes from checkpoints/).
# datasets.vla_data.* the data recipe this checkpoint was trained with (prompt template, pointmap
# options, dataset roots relative to the repository root).
# trainer.* the optimisation hyper-parameters that were used; informational for inference.
#
# The state fed to the action head is the 11-D end-effector anchor [position 3 | rotation (first two
# rows of the rotation matrix) 6 | gripper 2] sliced from the dataset state by key; the slices are
# derived from the data registry entry of datasets.vla_data.data_mix.
framework:
name: PointmapVLA
qwenvl:
base_vlm: ./base_vlm
attn_implementation: flash_attention_2
pointmap:
frame: base
action_model:
action_dim: 10
state_dim: 11
action_horizon: 8
repeated_diffusion_steps: 8
num_inference_timesteps: 4
add_pos_embed: true
max_seq_len: 1024
noise_beta_alpha: 1.5
noise_beta_beta: 1.0
noise_s: 0.999
num_timestep_buckets: 1000
diffusion_model_cfg:
action_dit_hidden_dim: 1024
attention_head_dim: 64
dropout: 0.2
final_dropout: true
interleave_self_attention: true
norm_type: ada_norm
positional_embeddings: null
datasets:
vla_data:
dataset_py: lerobot_datasets
lerobot_version: v3.0
include_state: true
data_root_dir: playground/Datasets/libero_r6
data_mix: libero_r6_pointmap
collate_pointmap: true
pointmap_data_root: playground/Datasets/libero_r6_pointmap
pointmap_out_hw:
- 256
- 256
obs_image_size:
- 224
- 224
collate_vlm_inputs: true
CoT_prompt: 'Task: {instruction} Mode: <end_effector>'
instruction_normalize: true
pointmap_origin_tag: ' Pointmap_origin: <robot>'
per_device_batch_size: 32
num_workers: 8
prefetch_factor: 6
video_backend: pyav
delete_pause_frame: false
trainer:
learning_rate:
action_model: 0.0001
base: 0.0001
pointmap_encoder: 1.0e-05
qwen_vl_interface: 1.0e-05
lr_scheduler_type: cosine_with_min_lr
max_train_steps: 30000
num_warmup_steps: 1500
optimizer:
betas:
- 0.9
- 0.999
eps: 1.0e-08
weight_decay: 1.0e-05
scheduler_specific_kwargs:
min_lr: 1.0e-06
gradient_accumulation_steps: 1
logging_frequency: 5
save_interval: 1000
save_full_state: true
full_state_interval: 1000
resume_keep: 1
is_resume: true
eval_interval: 1000000
freeze_modules: ''
seed: 42
|