pai-embedding-pick-fruits-policy

PAI-Embedding Native-60 Pick Fruits Policy — 100-Epoch Target

This repository contains the audited final 100-epoch checkpoint for the three-way Pick Fruits downstream-policy comparison. Training is capped at this milestone; later 200-epoch states are not used by this study. The visual encoder is frozen; the Qwen3.5-0.8B policy and action head are trained.

Artifact contract

• Checkpoint: checkpoint/epoch_0100.pt
• Step / epoch: 36,150 / 100
• Encoder: PAI-Embedding
• Encoder revision: 5d7e495e6e4b33b795d98b50588a06edef336a6f
• Alignment: native_60
• Encoder layout: {"image_tokens_per_camera": 60, "num_cameras": 3, "representation": "cosmos3_layer_28_native_dense_vision_tokens+fused_query_summary;egodex_domain_3_zero_action_scaffold", "text_tokens": 1, "width": 1024}
• Dataset: 100 Pick Fruits teleoperation episodes; 92,494 samples
• Effective batch: 256 on 8 H100 GPUs
• Comparison target: 100 epochs
• Cosine-schedule horizon used by all three matched runs: 200 epochs; the common epoch-100 prefix is retained to avoid changing LR midway through aligned-256
• Training loss at the epoch boundary: 0.02180 at step 36150
• Diagnostic: held-in diagnostic flow loss 0.03458 and MAE 0.00289 at step 36000
• Schema: sha256:f60ed2905e168e731f7c6f9371b542bb0966a82392ab56ea8af28eca48525735
• Normalization: sha256:bd52c50d4b22c8efafeb66054a82f4e9638a93cb4cd41009f406d5a140c9f2ad
• Checkpoint SHA-256: 3125ab555fe68511c84f0a9f34325de8907a7ec2c9766d1fbb2fda72b48549d6
• Training code: yunzeliu/VLA2Vec@537bd21418cb75eaea9672bbf513297ba081e4e8 on caip_downstream_policy

The diagnostic is computed on held-in training data. There is no held-out validation split, so it must not be reported as a generalization or robot success metric.

Export and inference

git clone --branch caip_downstream_policy https://github.com/yunzeliu/VLA2Vec.git
cd VLA2Vec
huggingface-cli download YunzeLiu/pai-embedding-pick-fruits-policy --local-dir /path/to/model
MODEL_DIR=/path/to/model

.venv/bin/python scripts/export_bundle.py   --checkpoint "$MODEL_DIR/checkpoint/epoch_0100.pt"   --out bundles/pai-embedding-pick-fruits-policy-epoch100   --encoder-pack assets/pai-embedding-encoder-pack --train-config "$MODEL_DIR/training/config.json"

HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1   .venv/bin/python scripts/serve.py   --bundle bundles/pai-embedding-pick-fruits-policy-epoch100   --encoder-pack assets/pai-embedding-encoder-pack --bind 'tcp://*:5678'

Validate the bundle with scripts/mock_client.py before hardware use. IK, collision checking, workspace limits and emergency-stop behavior remain the robot controller's responsibility.