Add model card for ACT

README.md

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+---
+library_name: lerobot
+license: apache-2.0
+tags:
+  - foundation
+  - amd
+  - rocm
+  - robotics
+pipeline_tag: robotics
+---
+
+![](assets/ACT.jpg)
+
+# ACT: Optimized for AMD ROCm
+
+ACT (Action Chunking Transformer, Zhao et al.) is a vision-only behavior-cloning policy from HuggingFace LeRobot for 6-DOF robot arm control. There is no language input, no task prompt, and no flow-matching denoiser. This repository packages evaluation/inference for robot arm action prediction using PyTorch, exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs and CPUs.
+
+This is based on the implementation of ACT found [here](https://huggingface.co/docs/lerobot/en/act).
+This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/Act) to reproduce results or export with custom configurations. More details on model performance can be found [here](#performance-summary).
+
+---
+
+## Task Overview
+
+**Task:** Robot arm action prediction (behavior cloning)
+
+**Dataset:** BlankHead/so101_redcube_greencloth_3cams (LeRobot format)
+
+**Output metrics:** MAE, RMSE (per-joint and per-episode)
+
+> **Backend note:** CPU runs FP32; GPU runs BF16. No NPU (VitisAI) path is available — NPU targets print an informational note and exit cleanly.
+
+---
+
+## AMD ROCm Optimization
+
+This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs. Key points:
+
+- Validated backends: **PyTorch** (native ROCm HIP kernels) — CPU (FP32) and GPU (BF16).
+- No code changes required versus the upstream ACT/LeRobot implementation — only environment/runtime configuration differs.
+- CPU fallback path supported for environments without a ROCm-capable GPU.
+- No NPU (VitisAI) path is available for this model.
+
+| Runtime | Precision | Backend | Hardware | Notes |
+|---|---|---|---|---|
+| CPU | FP32 | PyTorch | AMD CPU | `make benchmark-cpu` / `make evaluate-cpu` |
+| GPU | BF16 | PyTorch (ROCm) | AMD Instinct™ / Radeon™ GPU | `make benchmark-gpu` / `make evaluate-gpu` |
+
+---
+
+## Getting Started
+
+For setup instructions, evaluation scripts, and custom configuration options, see the [Act on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/Act).
+
+---
+
+## Model Details
+
+**Model Type:** Vision-only behavior-cloning policy (Action Chunking Transformer)
+
+**Base Model:** (ACT — Action Chunking Transformer, Zhao et al.)
+
+**Model Stats:**
+- Model variant: act-3cams-val (fine-tuned checkpoint, 3-camera SO-101 setup)
+- Vision-only input (side, up, wrist cameras) — no language input, no task prompt, no flow-matching denoiser
+- `chunk_size=100`, `n_action_steps=100`, `temporal_ensemble_coeff=null` (one chunk, all 100 actions consumed, no temporal ensemble)
+- Number of parameters: `80M`
+- Precision tested: FP32 (CPU), BF16 (GPU)
+
+---
+
+## Performance Summary
+
+Lower MAE/RMSE indicates predicted joint actions more closely match the recorded ground-truth trajectory; both are computed per-joint and averaged across episodes.
+
+### 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
+
+**Full Dataset Evaluation (BlankHead/so101_redcube_greencloth_3cams, 1 episode, chunked mode, no temporal ensemble)** — filled from `runs/eval/<dataset_tag>/loss.json`; run `make evaluate-<device>` to refresh:
+
+<!-- accuracy-table-start -->
+| Device | Backend | Precision | Variant | Avg MAE | Avg RMSE |
+|--------|---------|-----------|---------|---------|----------|
+| CPU/GPU | PyTorch | FP32/BF16 | act-3cams-val | 1.6267 | 4.4198 |
+<!-- accuracy-table-end -->
+
+Per-joint breakdown:
+
+| Joint | Avg MAE | Avg RMSE |
+|-------|---------|----------|
+| shoulder_pan | 1.6908 | 2.4138 |
+| shoulder_lift | 3.6184 | 8.7557 |
+| elbow_flex | 2.0769 | 5.3892 |
+| wrist_flex | 1.0396 | 1.7437 |
+| wrist_roll | 0.7511 | 1.0216 |
+| gripper | 0.5832 | 1.2611 |
+
+---
+
+## 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/Act)**
+
+The GitHub repository includes:
+- Setup and prerequisites for ROCm environments
+- Scripts for the supported runners (`pipeline.py`, `evaluate_lerobot.py`, `benchmark_lerobot.py`)
+- Additional model variants and datasets
+- Benchmarking and reproduction instructions
+
+---
+