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license: mit
pipeline_tag: robotics
---
# TokenHSI Checkpoint Instruction
[2025-04-07] All models have been released!
Please download the checkpoints and organize them as the following file structure:
```
|-- tokenhsi
|-- lpanlib
|-- body_models
|-- smpl
|-- SMPL_FEMALE.npz
|-- SMPL_MALE.npz
|-- SMPL_NEUTRAL.pkl
|-- ...
|-- output
|-- single_task
|-- ckpt_traj.pth
|-- ckpt_sit.pth
|-- ckpt_climb.pth
|-- ckpt_carry.pth
|-- tokenhsi
|-- ckpt_stage1.pth
|-- ckpt_stage2_comp_traj_carry
|-- ckpt_stage2_comp_sit_carry
|-- ckpt_stage2_comp_climb_carry
|-- ckpt_stage2_objShape_chair.pth
|-- ckpt_stage2_objShape_table.pth
|-- ckpt_stage2_terrainShape_traj.pth
|-- ckpt_stage2_terrainShape_carry.pth
|-- ckpt_stage2_longterm.pth
```
* We first provide four single-task environments, including path-following, sitting, climbing, and carrying. For each task, we train a specialized policy using AMP. The trained models are:
* single_task/ckpt_traj.pth
* single_task/ckpt_sit.pth
* single_task/ckpt_climb.pth
* single_task/ckpt_carry.pth
* We then provide our multi-task environment for training the versatile, unified transformer policy. The trained model is:
* tokenhsi/ckpt_stage1.pth
* Finally, we perform policy adaptation to generalize the learned skills to novel tasks. We list the trained models across various downstream tasks.
* Skill Composition
* Follow + Carry: tokenhsi/ckpt_stage2_comp_traj_carry.pth
* Sit + Carry: tokenhsi/ckpt_stage2_comp_sit_carry.pth
* Climb + Carry: tokenhsi/ckpt_stage2_comp_climb_carry.pth
* Object Shape Variation
* Chair: tokenhsi/ckpt_stage2_objShape_chair.pth
* Table: tokenhsi/ckpt_stage2_objShape_table.pth
* Terrain Shape Variation
* Follow: tokenhsi/ckpt_stage2_terrainShape_traj.pth
* Carry: tokenhsi/ckpt_stage2_terrainShape_carry.pth
* Long-horizon Task Completion
* tokenhsi/ckpt_stage2_longterm.pth
GitHub repo: https://github.com/liangpan99/TokenHSI
arXiv paper: https://huggingface.co/papers/2503.19901
Project page: https://liangpan99.github.io/TokenHSI/ |