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library_name: pytorch
license: gemma
tags:
- foundation
- amd
- rocm
- robotics
pipeline_tag: robotics
---
# pi0.5: Optimized for AMD ROCm
pi0.5 (PI05Policy, Physical Intelligence) is a vision-language-action policy from Hugging Face LeRobot for 6-DOF robot arm control. This repository packages inference for robot arm action prediction using **PyTorch**, exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs.
This is based on the implementation of pi0.5 found [here](https://huggingface.co/lerobot/pi05_base).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [pi0.5 AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/pi0.5) to reproduce results or export with custom configurations. More details on model performance can be found [here](#accuracy-pipeline).
---
## Task Overview
**Task:** Robot arm action prediction (vision-language-action)
**Dataset:** BlankHead/so101_redcube_greencloth_3cams (LeRobot format)
**Output metrics:** MAE, RMSE (per-joint and per-episode)
> **Model variants:** No `MODEL_SIZE` variants — the pipeline auto-detects PEFT/LoRA checkpoints via `adapter_config.json` and merges the adapter, patching `action_dim` to the checkpoint's value.
> **PyTorch note:** GPU only (ROCm — BF16); weights are streamed directly from disk into GPU VRAM in bf16, bypassing any CPU copy. A GPU with ≥32 GB VRAM is required — no CPU or NPU (VitisAI) path is available, since loading the ~14 GB fp32 weights on CPU would require >48 GB of system RAM. Requires Hugging Face auth (`HF_TOKEN`) since the tokenizer pulls the gated `google/paligemma-3b-pt-224` repo.
---
## AMD ROCm Optimization
This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**. Key points:
- Validated backend: **PyTorch** (native ROCm HIP kernels), BF16 precision, GPU only.
- No code changes required versus the upstream pi0.5 implementation — only environment/runtime configuration differs.
- No CPU or NPU fallback path is available for this model.
| Runtime | Precision | Backend | Hardware | Notes |
|---|---|---|---|---|
| PyTorch | BF16 | HIP (ROCm) | AMD Instinct™ / Radeon™ GPU (≥32 GB VRAM) | Direct-to-GPU weight streaming; no CPU/NPU path |
---
## Getting Started
For setup instructions, evaluation scripts, and custom configuration options, see the [pi0.5 on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/pi0.5).
---
## Model Details
**Model Type:** Vision-language-action policy for robot arm control
**Base Model:** `lerobot/pi05_base` (PI05Policy)
**Model Stats:**
- Precision tested: BF16 (GPU only)
- VRAM requirement: ≥32 GB (bf16 weight streaming, ~14 GB on-disk fp32 checkpoint)
- Configurable runtime knobs: `rtc_config.enabled` (Real-Time Chunking), `num_steps` (flow-matching denoising passes per chunk), `n_action_steps` (actions consumed per chunk)
---
## Accuracy Pipeline
Open-loop offline evaluation is fully implemented: `make evaluate-gpu` runs inference on recorded dataset episodes and computes per-joint and per-episode MAE / RMSE against the recorded ground-truth actions. Lower is better for both metrics.
### Metrics Explained
| Metric | Description |
|--------|-------------|
| MAE | Mean Absolute Error — average absolute difference between predicted and ground-truth joint positions across all timesteps. Lower is better. |
| RMSE | Root Mean Squared Error — penalizes large deviations more heavily than MAE. Lower is better. |
### Accuracy Results
**Published Results** — filled from `runs/eval/<dataset_tag>/loss.json`:
<!-- accuracy-table-start -->
| Dataset | Mode | Average MAE | Average RMSE |
|---------|------|--------------|--------------|
| BlankHead/so101_redcube_greencloth_3cams (1 episode) | select_action | 19.5420 | 25.2683 |
| HarikrishnaVydana/Lerobot-redcube-wite-bg-pickdrop-v3 (1 episode) | chunked_rtc | 19.2629 | 32.3233 |
<!-- accuracy-table-end -->
---
## Dig Deeper
Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?
📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/pi0.5)**
The GitHub repository includes:
- Setup and prerequisites for ROCm environments
- Direct-to-GPU bf16 weight streaming and PEFT/LoRA adapter merging
- Open-loop dataset evaluation with trajectory plots and comparison videos
- Latency benchmarking with Chrome trace output
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