LingBot-VA — Galaxea A1 Mango-to-Plate EEF — Step 100

LingBot-VA fine-tuned for red-mango placement onto a blue plate with a Galaxea A1 arm. The model predicts robot actions and video latents.

Data

Training demonstrations.

Field	Value
Episodes	26
Frames	8,783 at 30 FPS
Tasks	Put the red mango into the blue plate
Cameras	`front` 480×480 RGB; `wrist` 640×480 RGB
Action	Episode-relative EEF pose + continuous normalized gripper

Training epochs

Approximately 61.54 equivalent fine-tuning epochs, calculated as 100 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.

Files and configuration

transformer/ contains the step-100 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