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Kaz55/dexwild-dit-dg5f-ur5e-newcable-v5v6
dexwild-dit-dg5f-ur5e-newcable-v5v6 is a robotics model from Kaz55. Use it for the robotics task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
DiT (diffusion transformer) policy trained with the DexWild ditpolicy codebase on Kaz55/dg5fur5enewcablev5v6h5.
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Updated Aug 13, 2026
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From the Hugging Face model README
DiT (diffusion transformer) policy trained with the DexWild dit_policy codebase
on Kaz55/dg5f_ur5e_newcable_v5v6_h5.
| robot | UR5e (6 dof) + DG-5F right hand (20 dof) |
| state / action | 26 dims each — ur_arm_joint_states+dg5f_right_joint_states / ur_gello_joint_position+dg5f_right_joint_cmd |
| action space | joint_abs |
| cameras | realsense, realsense2, gelsight1, gelsight2 |
| vision encoder | ViT-base per camera (share_cam_features=False), init from SOUP_1M_DH.pth |
| action chunk | 60 |
| iterations | 200000 |
| batch size | 32 |
| data | 180 episodes / 208,933 transitions @ 30 Hz |
The two GelSight streams are their own cameras with their own ViT weights, so tactile features are never forced through the RGB scene encoders.
dg5f_newcable_v5v6_dit_ac60_200000.pthagent_config.yamlexp_config.yamlcombined_ac_norm.jsontrain_data_newcable_v5v6_ac_norm.jsontrain_data_newcable_v5v6_state_norm.jsoncombined_state_norm.jsonagent_config.yaml / exp_config.yaml are the resolved hydra configs the run
used; serve_policy.py in the training repo consumes the checkpoint directly.