File size: 4,164 Bytes
8919b2f | 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 | # PACE-SimplerEnv: model config of the released PACE checkpoint, read by the PACE code together with pytorch_model.pt
# and dataset_statistics.json in this directory:
# from pace.model.framework.base_framework import baseframework
# policy = baseframework.from_pretrained("./PACE-SimplerEnv")
# Training recipes and launchers are in the code repository (docs/TRAINING.md).
run_id: PACE-SimplerEnv
run_root_dir: runs
seed: 42
trackers: [jsonl] # "jsonl" -> <run>/metrics.jsonl; add "wandb" for Weights & Biases (entity: wandb_entity or $WANDB_ENTITY)
wandb_entity: null
wandb_project: pace
framework:
# "PACE"; the long alias "ProgressVLA_DinoTargetQueryProgressTripleReadoutFullGoal" is the name used by the
# original training runs and is still accepted.
name: PACE
qwenvl:
base_vlm: Qwen/Qwen3-VL-4B-Instruct # env BASE_VLM (local directory or HF id) takes precedence
attn_implementation: flash_attention_2 # env PACE_ATTN_IMPLEMENTATION (e.g. sdpa) overrides
action_model:
action_model_type: DiT-B # 12 heads x 64, 16 layers (diffusion_model_cfg.num_layers)
hidden_size: 1024
add_pos_embed: true
max_seq_len: 1024
action_dim: 7
state_dim: 7 # the state encoder exists in the checkpoint, but no proprioceptive state is fed
future_action_window_size: 15
action_horizon: 16 # action chunk H = future_action_window_size + 1
past_action_window_size: 0
noise_beta_alpha: 1.5
noise_beta_beta: 1.0
noise_s: 0.999
num_timestep_buckets: 1000
num_inference_timesteps: 4 # flow-matching (Euler) denoising steps
num_target_vision_tokens: 32
diffusion_model_cfg:
dropout: 0.2
final_dropout: true
interleave_self_attention: true
norm_type: ada_norm
num_layers: 16
output_dim: 1024
positional_embeddings: null
progress:
layer_idx: 18 # query readouts from the hidden states of VLM layer 18
num_goal_queries: 4 # K goal queries, mean-pooled into the foresight-anchor head
use_goal_dit_token: true # the predicted anchor is a DiT conditioning token (besides p_cur and dp_cur)
lambda_long: 0.1 # goal-anchor loss (MSE in DINOv2 space)
lambda_pcur: 0.1 # current-progress loss
lambda_dpcur: 0.1 # forward-commitment loss
lambda_cons: 0.1 # consistency: p_cur readout vs projection onto the predicted anchor
cons_detach_anchor: true
query_init_std: 0.01
train_query_token_rows_only: true # new token rows are trained; the rest of the VLM is fully fine-tuned
progress_future_offset: 15 # Delta (history / commitment horizon in env steps); equals datasets.vla_data value
use_history_images: true # the frame at t - Delta is an extra VLM input
use_dp_prev_input: true # the historical pace dp_prev enters the VLM as a token
use_full_goal_anchor_at_inference: true
infer_history_mode: step_aligned
dino_variant: vitb14 # frozen DINOv2 ViT-B/14 (progress geometry)
dino_target_dim: 768
dino_head_hidden_dim: 1024
dino_repo_root: null # null -> vendored third_party/dinov2 (env DINO_REPO_ROOT overrides)
dino_weights: null # null -> $PACE_CACHE/dinov2 download (env DINOV2_WEIGHTS overrides)
# Commitment audit (inference-time failure detector, never changes actions): alarm when the shortfall CUSUM
# exceeds audit_threshold or p_cur has not risen for more than audit_stall_chunks action chunks.
# audit_threshold is shared by all benchmarks (unmet commitments adding up to 0.4 of the task); the stall limit is
# calibrated per benchmark on held-out checkpoints (2.5% false alarms on successful episodes); see docs/PROGRESS.md.
audit_kappa: 0.0
audit_threshold: 0.4
audit_stall_chunks: 2.12
datasets:
vla_data:
dataset_py: lerobot_datasets
image_size: [224, 224] # inference-time resize of the client images
CoT_prompt: "Your task is {instruction}. To identify the key objects for your task. Locate their bounding boxes in [x1,y1,x2,y2] format."
return_progress_anchors: true
progress_future_offset: 15
trainer: # inference only reads these
repeated_diffusion_steps: 4
pretrained_checkpoint: null
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