Instructions to use chennana1028/lingbot-va-openarm-sbint-eef-step20000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chennana1028/lingbot-va-openarm-sbint-eef-step20000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("chennana1028/lingbot-va-openarm-sbint-eef-step20000", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
LingBot-VA OpenArm SBInt EEF step-20000
Fine-tuned transformer/ (bf16) of LingBot-VA on SBInt openarm002 dual-arm data.
- Route: relative EEF (xyz + quat xyzw, relative to sample-window first frame)
- gripper, 16-d left-first:
[l_xyz3, l_quat4, lgrip, r_xyz3, r_quat4, rgrip]
- gripper, 16-d left-first:
- Init:
robbyant/lingbot-va-posttrain-robotwin - Train: config
openarm_eef_train, 20000 steps, 8xH100, global batch 8, lr 1e-5 - Open-loop ep740: rel MAE 0.0052, seam 1.22x, abs xyz MAE 0.0040 m
- Parent repo: https://github.com/zhujohn9604/lingbot-va-train
(data/IK/deploy:
handoff/README.md,handoff/DEPLOY_IK.md)
Joint-space counterpart: chennana1028/lingbot-va-openarm-sbint-step20000.
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