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--- |
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license: apache-2.0 |
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task_categories: |
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- robotics |
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--- |
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<div align="center"> |
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<h1> ALMI-X </h1> |
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</div> |
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<h5 align="center"> |
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<a href="https://almi-humanoid.github.io/">🌍website</a>  <a href="https://github.com/TeleHuman/ALMI-Open/">📊code</a>   <a href="https://arxiv.org/abs/2504.14305">📖paper</a> |
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</h5> |
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# Overview |
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We release a large-scale whole-body motion control dataset - ALMI-X, featuring high-quality episodic trajectories from MuJoCo simulations deployable on real robots, based on our humanoid control policy - ALMI. |
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# Dataset Instruction |
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We collect ALMI-X dataset in MuJoCo simulation by running the trained ALMI policy. In this simulation, we combine a diverse range of upper-body motions with omnidirectional lower-body commands, and employ a pre-defined paradigm to generate corresponding linguistic descriptions for each combination. (i) For the upper-body, we collect data using our upper-body policy to track various motions from a subset of the AMASS dataset, where we remove entries with indistinct movements or those that could not be matched with the lower-body commands, such as `push from behind`. |
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(ii) For the lower-body, we first categorize command directions into several types according to different combination of linear and angular velocity command and define 3 difficulty levels for command magnitudes, then the lower-body command is set by combining direction types and difficulty levels. |
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Overall, each upper-body motion from the AMASS subset is paired with a specific direction type and a difficulty level serving as the inputs of policy to control the robot. In addition, trajectories in which the lower body `stand still` while the upper body tracks motions are also incorporated into the dataset. Each language description in ALMI-X is organized as `"[movement mode] [direction] [velocity level] and `motion`"}, each of which corresponds to the data collected from a trajectory lasting about 4 seconds with 200 steps. For each trajectory$, we run two policies (i.e., lower policy and upper policy) based on the commands obtained from the aforementioned combinations to achieve humanoid whole-body control. |
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# How to Use Dataset |
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- We release all of the text description data `text.tar.gz`; the trajectory data `data.tar.gz` with robot states, actions, DoF position, global position and global orientation informations. |
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- We release the train set split `train.txt` |
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Here we offer a simple demo code to introduce the data formats in the dataset: |
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``` python |
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import numpy as np |
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data = np.load("data_path"+"/xxx.npy", allow_pickle=True) |
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data.item()['obs'] # [frame_nums, 71] |
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data.item()['actions'] # [frame_nums, 21] |
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data.item()['dof_pos'] # [frame_nums, 21] |
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data.item()['root_trans'] # [frame_nums, 3] |
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data.item()['root_rot'] # [frame_nums, 4] |
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``` |
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# Dataset Statistics |
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Percentage of steps for different categories of motions before and after data augmentation. |
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<br> |
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The visualization of $x-y$ coordinates of the robot for each step in the dataset. We down-sample the data for visualization. |
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<br> |
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# Dataset Collection Pipeline |
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We release our datase collection code at our github repository: <a href="https://github.com/TeleHuman/ALMI-Open/tree/master/Data_Collection">Data_Collection</a>. |
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# Citation |
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If you find our work helpful, please cite us: |
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```bibtex |
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@misc{shi2025almi, |
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title={Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning}, |
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author={Jiyuan Shi and Xinzhe Liu and Dewei Wang and Ouyang Lu and Sören Schwertfeger and Fuchun Sun and Chenjia Bai and Xuelong Li}, |
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year={2025}, |
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eprint={2504.14305}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.RO}, |
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url={https://arxiv.org/abs/2504.14305}, |
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} |
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} |
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``` |
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