---
license: apache-2.0
task_categories:
- robotics
---
ALMI-X
# Overview
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.
# Dataset Instruction
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`.
(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.
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.
# How to Use Dataset
- 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.
- We release the train set split `train.txt`
Here we offer a simple demo code to introduce the data formats in the dataset:
``` python
import numpy as np
data = np.load("data_path"+"/xxx.npy", allow_pickle=True)
data.item()['obs'] # [frame_nums, 71]
data.item()['actions'] # [frame_nums, 21]
data.item()['dof_pos'] # [frame_nums, 21]
data.item()['root_trans'] # [frame_nums, 3]
data.item()['root_rot'] # [frame_nums, 4]
```
# Dataset Statistics
Percentage of steps for different categories of motions before and after data augmentation.
The visualization of $x-y$ coordinates of the robot for each step in the dataset. We down-sample the data for visualization.
# Dataset Collection Pipeline
We release our datase collection code at our github repository: Data_Collection.
# Citation
If you find our work helpful, please cite us:
```bibtex
@misc{shi2025almi,
title={Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning},
author={Jiyuan Shi and Xinzhe Liu and Dewei Wang and Ouyang Lu and Sören Schwertfeger and Fuchun Sun and Chenjia Bai and Xuelong Li},
year={2025},
eprint={2504.14305},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2504.14305},
}
}
```