Instructions to use AlexWortega/tinyvla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use AlexWortega/tinyvla with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| #!/usr/bin/env python | |
| """UAV (quadrotor) few-shot: does morphology conditioning (C) beat learned-ID (B) | |
| when the held-out robot is a genuinely different MORPHOLOGY (flying free-body, not | |
| another arm)? The jaco result (C≈B) is expected — jaco's descriptor ≈ training | |
| arms. The uav descriptor is far outside the training cluster, so this is the real | |
| test of C>B. | |
| For each variant in {B, C} and N in {100, 500}: FT the pretrained final on uav[0:N], | |
| eval endpoint error on uav[test:test+20]. A is skipped (native already shown to | |
| diverge; and uav native == canonical 6-DOF so it'd be redundant). | |
| """ | |
| from __future__ import annotations | |
| import json, subprocess | |
| from pathlib import Path | |
| ROOT = Path("/home/alexw/tinyvla") | |
| PY = str(Path.home() / "tinyvla_venv/bin/python") | |
| UAV = "heldout_uav" | |
| UAV_ROOT = str(Path.home() / "tinyvla_data/heldout/heldout_uav") | |
| TEST_START = 1250 # 1275 eps; test on last 20 (leave a gap), FT on first N | |
| SIZES = [100, 500] | |
| VARIANTS = { | |
| "B": {"dir": "tv2_B_id_canon", "cond": "id", "space": "canonical", "emb": 4, "mkey": None}, | |
| "C": {"dir": "tv2_C_morph_canon", "cond": "morph", "space": "canonical", "emb": 0, "mkey": "uav"}, | |
| } | |
| OUT = ROOT / "outputs" / "uav_fewshot_results.json" | |
| def write_config(v, spec, n): | |
| cfg = ROOT / "outputs" / f"uav_{v}_{n}.yaml" | |
| extra = f" morph_key: {spec['mkey']}\n" if spec["mkey"] else "" | |
| cfg.write_text(f"""output_dir: {ROOT}/outputs/uav_{v}_{n} | |
| wandb: null | |
| seed: 42 | |
| morphology_descriptors: {ROOT}/configs/morphology/descriptors.yaml | |
| resume_from: {ROOT}/outputs/{spec['dir']}/final | |
| resume_step: 0 | |
| datasets: | |
| - repo_id: {UAV} | |
| root: {UAV_ROOT} | |
| episodes: {n} | |
| weight: 1.0 | |
| embodiment_id: {spec['emb']} | |
| {extra}policy: | |
| chunk_size: 50 | |
| n_action_steps: 50 | |
| image_size: 256 | |
| freeze_lm: true | |
| freeze_vision_encoder: true | |
| num_embodiments: 16 | |
| conditioning: {spec['cond']} | |
| action_space: {spec['space']} | |
| batch_size: 32 | |
| grad_accum: 1 | |
| num_workers: 8 | |
| lr: 5.0e-5 | |
| warmup_steps: 100 | |
| steps: 5000 | |
| grad_clip: 10.0 | |
| log_freq: 500 | |
| save_freq: 5000 | |
| mixed_precision: bf16 | |
| """) | |
| return cfg | |
| def run(cmd, log): | |
| with open(log, "w") as f: | |
| return subprocess.run(cmd, stdout=f, stderr=subprocess.STDOUT).returncode | |
| def main(): | |
| res = {} | |
| # zero-shot (N=0) baselines first | |
| for v, spec in VARIANTS.items(): | |
| ev = ROOT / "outputs" / f"uav_{v}_0_eval.log" | |
| cmd = [PY, str(ROOT/"scripts/eval_canonical.py"), "--checkpoint", | |
| str(ROOT/f"outputs/{spec['dir']}/final"), "--dataset", UAV, "--root", UAV_ROOT, | |
| "--ep-start", str(TEST_START), "--episodes", "20"] | |
| cmd += ["--morph-key", "uav"] if spec["mkey"] else ["--oracle-ids", "4"] | |
| print(f"[ZS] {v} N=0", flush=True); run(cmd, ev) | |
| line=[l for l in ev.read_text().splitlines() if "BEST" in l] | |
| res[f"{v}_0"]={"eval":line[-1] if line else "?"}; print(" ",res[f"{v}_0"]); OUT.write_text(json.dumps(res,indent=1)) | |
| # few-shot | |
| for v, spec in VARIANTS.items(): | |
| for n in SIZES: | |
| tag=f"{v}_{n}"; cfg=write_config(v,spec,n) | |
| print(f"[FT] {tag}", flush=True); run([PY,str(ROOT/"scripts/train.py"),"--config",str(cfg)], ROOT/f"outputs/uav_{tag}_ft.log") | |
| ckpt=ROOT/f"outputs/uav_{tag}/final" | |
| if not ckpt.exists(): res[tag]={"error":"no ckpt"}; continue | |
| ev=ROOT/f"outputs/uav_{tag}_eval.log" | |
| cmd=[PY,str(ROOT/"scripts/eval_canonical.py"),"--checkpoint",str(ckpt),"--dataset",UAV,"--root",UAV_ROOT,"--ep-start",str(TEST_START),"--episodes","20"] | |
| cmd += ["--morph-key","uav"] if spec["mkey"] else ["--oracle-ids","4"] | |
| print(f"[EVAL] {tag}", flush=True); run(cmd, ev) | |
| line=[l for l in ev.read_text().splitlines() if "BEST" in l] | |
| res[tag]={"eval":line[-1] if line else "?"}; print(" ",res[tag]); OUT.write_text(json.dumps(res,indent=1)) | |
| print("\n=== UAV FEW-SHOT DONE ==="); [print(k,v) for k,v in res.items()] | |
| if __name__ == "__main__": | |
| main() | |