gate_setting1_chunksize25_batch32_from20000

gate_setting1_chunksize25_batch32_from20000

Task-conditioned text-token-gate OpenVLA-OFT model for real-world XArm, setting 1: pick cube and place into plastic cup. Trained fresh for 10000 steps on top of the oft_setting1_chunksize25_batch32_20k OFT backbone (action head / proprio projector / FiLM re-trained; cosine LR 5e-4 with 1000 warmup, effective batch 32, seed 6).

Gate configuration (baked into config.json)

• Gazing: self_attention (dim 512, 8 heads, 1 layer), gate MLP hidden 512 / depth 1
• Text pooling: contrastive_alignment_score (visual τ 0.1, text τ 0.05), instruction-only pooling, stopword filtering
• Layer gate: threshold mode, threshold 0.3, strength 0.5
• Budget loss: target 0.4, weight 0.01

⚠️ Serving requires the Task-conditioned_Gate codebase

The gate modules live in this repo's modeling_prismatic.py (loaded via trust_remote_code), but the openvla-oft serve flow syncs the local repo's modeling files INTO the checkpoint before loading. Serve with REPO_DIR pointing at the Task-conditioned_Gate repo — serving with vanilla openvla-oft silently drops the gate weights.

Contents

• Merged full model (model-*.safetensors, gate config in config.json)
• lora_adapter/ — LoRA + gate weights (modules_to_save=["text_token_gate"]), standalone copy
• action_head--10000_checkpoint.pt, proprio_projector--10000_checkpoint.pt, vision_backbone--10000_checkpoint.pt (FiLM)
• dataset_statistics.json, oft_training_config.json (chunk 25, action dim 7, proprio dim 6, BOUNDS_Q99)