pi05_hsr_base
pi05_hsr_base is a continual-pre-training checkpoint derived from the public pi0.5 vision-language-action (VLA) model for downstream mobile manipulation with the Toyota Human Support Robot (HSR). It is trained with AIRoA-formatted, dual-camera teleoperation data and is intended to serve as an initialization for task-specific real-robot adaptation.
Model summary
| Property | Value |
|---|---|
| Base checkpoint | Public pi0.5 checkpoint |
| Model type | Vision-language-action policy with flow matching |
| Target embodiment | Toyota Human Support Robot |
| Continual-pre-training data | AIRoA 75-task training collection |
| Continual-pre-training duration | Approximately 0.8 epoch |
| Sampling | Task-uniform, then episode sampling within each task |
| Global batch size | 2,048 |
| Training hardware | 64 NVIDIA H200 GPUs |
| Trainable components | Action expert; LoRA adapters on separate head- and hand-camera vision encoders |
| Frozen component | Language-model backbone |
| Model inputs used | Head RGB, hand RGB, proprioception, task instruction |
| Intended role | Initialization for downstream task-specific fine-tuning |
Architecture and training
pi05_hsr_base starts from the public pi0.5 checkpoint and retains its flow-matching training objective. During continual pre-training:
- the language-model backbone is frozen;
- the action expert is updated;
- separate vision encoders for the head and hand camera streams receive LoRA adapters; and
- tasks are sampled uniformly before sampling episodes within each task.
Task-uniform sampling prevents the long-tailed frequency distribution of the corpus from making the largest task families dominate optimization. The reported checkpoint was trained for approximately 0.8 epoch with a global batch size of 2,048 on 64 NVIDIA H200 GPUs.
The model consumes RGB observations and robot proprioception.
Training data
The checkpoint is continually pre-trained on a 75-task training collection drawn from the AIRoA mobile-manipulation collection effort, packaged in the AIRoA dual-camera teleoperation schema (head RGB, hand RGB, and proprioception). This training data is publicly released as AIRoA MoMa 5k.
Inputs and outputs
Inputs used during training
- natural-language task instruction;
- head-camera RGB observation;
- hand-camera RGB observation; and
- robot proprioceptive state.
Output
The checkpoint predicts model-native robot actions through the pi0.5 action expert.
License
pi05_hsr_base is released under the Gemma Terms of Use. Use, redistribution, and derivative works of these weights are subject to those terms and to the Gemma Prohibited Use Policy.
Citation
If you use pi05_hsr_base, please cite both the AIRoA MoMa dataset and pi0.5.
Training dataset: airoa-org/airoa-moma-5k
@article{takanami2025airoamoma,
author = {Takanami, Ryosuke and Khrapchenkov, Petr and Morikuni, Shu and others},
title = {{AIRoA MoMa} Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation},
journal = {arXiv preprint arXiv:2509.25032},
year = {2025}
}
@article{physicalintelligence2025pi05,
author = {{Physical Intelligence} and Black, Kevin and others},
title = {{$\pi_{0.5}$}: A Vision-Language-Action Model with Open-World Generalization},
journal = {arXiv preprint arXiv:2504.16054},
year = {2025}
}