LingBot-VA — Galaxea A1 Mango Placement EEF — Step 200
LingBot-VA fine-tuned for red-mango placement into a bowl or plate with a Galaxea A1 arm. The model predicts robot actions and video latents.
Data
| Field | Value |
|---|---|
| Episodes | 52 |
| Frames | 17,081 at 30 FPS |
| Tasks | Red mango to blue plate; red mango to bowl |
| 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
200 optimizer updates × 16 effective global batch size / 52 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.
Evaluation
Real-robot rollouts and object-grounding analysis.
Files and configuration
transformer/ contains the step-200 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
License
Apache License 2.0. See LICENSE.txt.