--- library_name: physicalai-train tags: - vision-language-action - robotics - physicalai license: apache-2.0 --- # Action Chunking Transformer That's a basic model for solving simplest imitation learning tasks. The original implementations can be found [here](https://github.com/tonyzhaozh/act/tree/main). The model takes images from one or multiple cameras and robot state and produces a chunk of actions, which robot can execute as a sequence of movements in real world. The model weights are random and provided only for testing purposes. To fine-tune your model with a custom dataset, you can use [Physical AI Studio](https://github.com/open-edge-platform/physical-ai-studio). # How to Use ## Installation ```bash uv pip install physicalai numpy ``` ## Running Inference with [OpenVINO Physical AI framework](https://github.com/openvinotoolkit/physicalai). The following API example showcases inference API for this model: ```python import numpy as np from physicalai.inference import InferenceModel model = InferenceModel("act-fp16-ov", device="CPU") # Build a dummy LIBERO-style observation. # LIBERO provides two cameras (agentview + wrist) and an 8-dim robot state. # Images use the LeRobot convention: float32 in [0, 1], shape (C, H, W). observation = { "images.image": np.random.rand(1, 3, 256, 256).astype(np.float32), "images.image2": np.random.rand(1, 3, 256, 256).astype(np.float32), "state": np.zeros((1, 8), dtype=np.float32), } chunk = model.predict_action_chunk(observation) ``` Note that the model should be downloaded and saved to the `act-fp16-ov` folder prior to running this script. ## Legal information The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. ## Disclaimer Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.