π₀.₅ (lerobot/pi05base) full fine-tune on mliu48/telearms-yam-cube-v4 (53,832 bimanual YAM demonstrations, 48 tasks, 30 Hz, 3 × 224×224 cameras). LeRobot 0.6.1 @ e40b58a8 with the repo's patch (tools/installlerobotpatch.py). - Full fine-tune (LLM + vision + action expert), bf16, gradient checkpointing. - 16 × H100 (2 nodes × 8), batch 16/GPU (global 256), 99,486 steps (one selected-data epoch). - Peak LR 7.07e-5, cosine decay to 7.07e-06, 1,000 warmup steps; relative actions (absolute grippers). Final checkpoint (step 99,486), training/evaluatepi.py suite settings in the RoboEval YAM simulator: 10 episodes per task, seeds 900000001–900000010, 1,800-step limit, 25-step action chunks, success…
Independent publisher
Helen Wang
helen9975
Models in Library2
Datasets in Library0
Models on Hugging Face13
Followers3
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Published tasks: Robotics.
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Models
MolmoAct2 full fine-tune of allenai/MolmoAct2-BimanualYAM on mliu48/telearms-yam-cube-v4 (53,832 simulated bimanual YAM demonstrations, 48 tasks, 30 Hz, 3 × 224×224 cameras). converted with olmo.hfmodel.convertmolmoact2tohf. - Full fine-tune (LLM, ViT, connector, LM head, action expert), unpacked + dynamic sequence length. - 16 × H100 (2 nodes × 8), batch 16/GPU (global 256), 100k-step schedule; LRs: LLM 2e-5, ViT 1e-5, connector 1e-5, action expert 1e-4 (upstream full-FT recipe × sqrt(256/64)). - Mixture tag telearmsyamsim: absolute joint-pose control, 14-D state/action (per arm 6 joints + gripper), cameras [camerafront, cameraleft, cameraright] (same order as BimanualYAM's [top, left…