--- library_name: lerobot tags: - lerobot - robotics - manipulation - visual-tactile - pi0 - bimanual license: apache-2.0 --- # pi05_bi_vitac — clean_smash_24 (Step 60k) Checkpoint at **step 60,000 / 250,000** of a Pi0.5 bimanual visual-tactile policy fine-tuned on 24 manipulation datasets covering clean and smash task families. ## Model Architecture | Component | Details | |---|---| | Base model | Pi0.5 (`pi05`) | | Vision-language backbone | PaliGemma (Gemma 2B) | | Action expert | Gemma 300M | | Tactile encoder | AnyTouch CLIP-B/16, 2-frame variant (full fine-tune) | | AnyTouch pool tokens | 14 | | State dim | 20 | | Action dim | 20 | | Action horizon | 50 | Visual observations pass through SigLIP; tactile observations pass through AnyTouch. The AnyTouch encoder is fully fine-tuned (LoRA rank = 0). ## Training Details | Hyperparameter | Value | |---|---| | Total steps | 250,000 | | This checkpoint | 60,000 | | Batch size | 128 (2 × 64, FSDP) | | Optimizer | AdamW | | Weight decay | 1e-4 | | Gradient clip norm | 1.0 | | LR schedule | Cosine decay | | Warmup steps | 5,000 | | Peak LR | 3e-5 | | Decay steps | 250,000 | | Final LR | 6e-7 | | EMA decay | 0.999 | | Save interval | 10,000 steps | | Base weights | `gs://openpi-assets/checkpoints/pi05_base/params` | ## Training Data 24 LeRobot datasets from [EricChen06](https://huggingface.co/EricChen06), covering two task families: **Clean** (pick-and-place): `green_clean_01–04`, `red_clean_01–04`, `blue_clean_01–04` **Smash**: `white_smash_01/03/04/05`, `black_smash_01–04`, `yellow_smash_01–04` ## Checkpoint Contents ``` 60000/ ├── _CHECKPOINT_METADATA ├── assets/ ├── params/ # EMA model parameters (for inference) └── train_state/ # Full optimizer state (for resuming training) ``` Use the `params/` sub-tree for inference and the `train_state/` sub-tree only if resuming training. ## Usage This checkpoint is designed for use with [ManiSkill-vitac](https://github.com/your-org/ManiSkill-vitac) and the `openpi` training framework. ```bash # Inference python deploy_scripts/infer.py \ --checkpoint_path /path/to/60000 \ --config pi05_bi_vitac ``` To resume training from this checkpoint, point `weight_loader` at the downloaded `60000/` directory. ## Citation If you use this model, please cite the underlying Pi0.5 and AnyTouch works.
