model card

README.md

@@ -0,0 +1,32 @@
+---
+license: other
+tags: [gr00t, isaac-gr00t, vla, isaac-lab, simulation, ffw-sg2, robotics]
+base_model: nvidia/GR00T-N1.7-3B
+---
+# groot-blockpick-clean
+
+GR00T N1.7 (3B) fine-tuned on the **clean** 500-demo top-down block-pick set (`padiredd/pick-cube-topdown-25cm-500-3cam`:
+ROBOTIS FFW SG2 in Isaac Lab, left-arm top-down pick of a lime-green block, 3 cameras at 240x240, 77,220 frames, fixed
+start pose, block uniform over x 0.23-0.48 / y 0.065-0.315 m, every demo with the same timing).
+Checkpoints live in `step-<n>/` folders (this upload: `step-20000`); each folder loads with the ROBOTIS fork of Isaac-GR00T
+(`omkarpatil18/Isaac-GR00T-n1.7` at e81d02b) via `Gr00tPolicy(embodiment_tag="new_embodiment", model_path=<folder>)` after
+registering the modality config `ffw_bg2_pick_cube_3cam_config.py` (state = arm_left 8 + arm_right 8, action = the same 16
+absolute joint targets, chunk 16, language "pick up the lime green block with the left arm").
+
+## Fine-tune recipe
+| | |
+|---|---|
+| base model | nvidia/GR00T-N1.7-3B (Cosmos-Reason2-2B backbone frozen; projector + diffusion action head tuned, 1.62 B trainable) |
+| steps / batch | 100,000 planned, global batch 32, checkpoint every 20k |
+| optimizer | lr 1e-4 cosine, warmup 5 %, weight decay 1e-5, AdamW, fp32 weights |
+| image aug | colour jitter brightness 0.4 / contrast 0.4 / saturation 0.5 / hue 0.1 + 5 deg random rotation + the repo's random crop |
+| state | 1 deg Gaussian noise on the 7 left-arm joints during training (our hook), state dropout 0.2 (repo default) |
+| data sampling | shard 1024, episode sampling rate 0.1, 4 loader workers |
+| hardware | NVIDIA A40 48 GB, 1.35 s/step |
+
+Files per step folder: model shards (fp32, 12.6 GB), config.json, processor_config.json, statistics.json (normalization),
+embodiment_id.json, experiment_cfg/, trainer_state.json. Optimizer, scheduler and RNG states are not uploaded.
+
+## Evaluation
+Sim rollouts (50 picks, same protocol as the diffusion policies in `padiredd/pick-cube-topdown-25cm-*-dp`) are run with
+`data_collection/rollout_policy.py --policy groot` against `groot_server.py`; results are added here as they come in.