Instructions to use Chanho-Lee/lges_case_pick_0708_rl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Chanho-Lee/lges_case_pick_0708_rl with LeRobot:
- Notebooks
- Google Colab
- Kaggle
Reversed-curiosity RL assets for LGES case pick (icm_0708.pt / critic_0708.pt)
Companion checkpoints for RL fine-tuning of Chanho-Lee/smolvla_naive_0708
(reversed-curiosity / anti-exploration reward, AWR + BC). Code:
smolvla_meanflow plugin.
| file | class | contents |
|---|---|---|
icm_0708.pt |
smolvla_meanflow.icm.ICM |
frozen forward models, streams ['proprio', 'vision'], per-stream calibrated eta {'proprio': 1.6751, 'vision': 0.3907} |
critic_0708.pt |
smolvla_meanflow.critic.CriticEnsemble |
V(s) on the fixed reward mix {'proprio': 1.0, 'vision': 1.0} (input: concat features, 975-d) |
Reward: r = sum_s w_s * exp(-eta_s * err_s) / sum_s w_s in (0, 1] per step.
Trained on Chanho-Lee/lges_case_pick_0708 with the last episode of each
pallet layer held out ([14, 29, 44, 59, 74]).
from huggingface_hub import hf_hub_download
from smolvla_meanflow.icm import ICM
from smolvla_meanflow.critic import CriticEnsemble
icm = ICM.load(hf_hub_download("Chanho-Lee/lges_case_pick_0708_rl", "icm_0708.pt"))
critic = CriticEnsemble.load(hf_hub_download("Chanho-Lee/lges_case_pick_0708_rl", "critic_0708.pt"))