RACE_main_robocasa
pi0.5 checkpoints on RoboCasa H50 (25 single-stage atomic tasks, human teleop, ~55 demos/task,
1382 episodes). PyTorch (openpi PI0Pytorch) safetensors; norm stats included under
assets/robocasa/robocasa_h50/.
| folder | what | notes |
|---|---|---|
teacher_ac10_30k_pytorch/ |
pi0.5 teacher, action_horizon 10, 30k steps from pi05_base, batch 64 |
PyTorch export (fp32) of the JAX checkpoint in SeonghoonYu/RACE_Robocasa/teacher_ac10_30k |
ft_vlmfreeze_h10_20k/ |
plain fine-tune of the teacher, VLM (SigLIP + Gemma) frozen, action expert trained, horizon 10, 20k steps | model-only (no optimizer) |
ft_vlmfreeze_h15_20k/ |
same, horizon 15 | model-only |
ft_vlmfreeze_h20_20k/ |
same, horizon 20 | model-only |
Fine-tune hyperparameters (all three): batch 64, constant LR 5e-5 (no warmup), AdamW, grad clip 1.0, bf16, 20k steps, initialised from the teacher.
RoboCasa eval (25 tasks x 50 trials, held-out object split B, open-loop chunk = horizon):
| checkpoint | replan | success |
|---|---|---|
| teacher_ac10_30k | 5 | 60.4% |
| teacher_ac10_30k | 10 | 62.6% |
| ft_vlmfreeze_h10_20k | 10 | 62.5% |
| ft_vlmfreeze_h15_20k | 15 | 62.1% |
| ft_vlmfreeze_h20_20k | 20 | 58.6% |