lingbot-va-galaxea-a1-plug-insertion-eef-step-500

LingBot-VA — Galaxea A1 Plug Insertion EEF — Step 500

LingBot-VA fine-tuned for picking up a charger and inserting it into the leftmost socket of a power strip with a Galaxea A1 arm. The model predicts robot actions and video latents.

Data

Training demonstrations.

Training epochs

Approximately 307.69 equivalent fine-tuning epochs, calculated as 500 optimizer updates × 16 effective global batch size / 26 training segments. Each selected episode contributes one latent training segment; the denominator is the segment count, not the raw video-frame count. This estimate assumes all selected segments have valid latent caches and excludes base-model pretraining. See training_summary.json.

Only the 26 training episodes are counted; the five reserved episodes are excluded. Random crops of at most 64 latent frames mean this is a segment-sampling epoch count, not a count of complete passes over every raw frame.

Evaluation

Real-robot evaluation on August 8, 2026: 1/12 successful insertions (8.3%). Five additional runs stopped during the initial grasp and were excluded. See the rollouts and failure analysis.

Files and configuration

transformer/ contains the step-500 weights. The base tokenizer, text encoder, and VAE are included. Use configs/va_a1_cfg.py for the EEF action mapping and normalization.

Training details · Training summary

The saved training code is in training/; this is an inference checkpoint without optimizer state.

License

Apache License 2.0. See LICENSE.txt.