--- 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/