| ## 1. Introduction |
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| The ATEC 2026 Simulation Challenge provides a standardized suite of robot simulation environments built on IsaacLab, designed to evaluate both locomotion and loco-manipulation capabilities. Participants may select one or multiple legged robot platforms to complete a set of representative tasks, including *Off-road Navigation*, *Tabletop Manipulation*, *Garbage Collection*, and *Obstacle Traversal*. |
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| This repository includes simulation assets, task definitions, and reference scripts to support development, evaluation, and submission. |
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| ### 1.2 Robots and Sensors |
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| - **Robot platforms** |
| - Humanoid: Unitree G1 (with two-finger gripper) |
| - Dual-wheel legged + manipulator: Tron1 + AgileX Piper |
| - Tron2A legged / wheel + manipulator |
| - Quadruped + manipulator: Unitree B2 + AgileX Piper |
| - Wheel-legged quadruped + manipulator: Unitree B2W + AgileX Piper |
| - Manipulator-only: AgileX Piper |
| - **Sensor suite** (standardized across platforms) |
| - 1 × LiDAR |
| - 1 × eye-to-hand RGB-D camera |
| - 1 × eye-in-hand RGB-D camera *(humanoids use a stereo pair)* |
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| ## Robot Platforms |
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| | Humanoid | Dual-wheel legged + manipulator | Tron2A legged + manipulator | Tron2A wheel + manipulator | Quadruped + manipulator | Wheel-legged quadruped + manipulator | Manipulator | |
| | :----------------------------------------------------: | :-------------------------------------------------------: | :-----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------------------: | :-------------------------------------------------------: | |
| | <p align="center"><img src="./doc/G1.png" width="180"> | <p align="center"><img src="./doc/Tron1.png" width="180"> | <p align="center"><img src="./doc/Tron2Legged.png" width="180"> | <p align="center"><img src="./doc/Tron2Wheel.png" width="180"> | <p align="center"><img src="./doc/B2.png" width="180"> | <p align="center"><img src="./doc/b2w.png" width="180"> | <p align="center"><img src="./doc/piper.png" width="180"> | |
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| > **Note:** Users may modify or optimize assets (e.g., collision geometry simplification) for training purposes. The provided assets serve as reference models for evaluation. |
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| ### 1.3 Challenge Arenas |
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| | Arena | Video | Arena | Video | |
| | --- | --- | --- | --- | |
| | Task A · Off-road Navigation | <p align="center"><img src="./doc/task_a.gif" width="180"> | Task E · Tabletop Manipulation | <p align="center"><img src="./doc/baseline_act.gif" width="180"> | |
| | Task B · Garbage Collection | <p align="center"><img src="./doc/task_b.gif" width="180"> | Task D · Obstacle Traversal | <p align="center"><img src="./doc/task_d.gif" width="180"> | |
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| > **Note:** For each task, participants are free to select any supported robot morphology. |
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| --- |
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| ### 1.4 Environment Matrix |
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| The `atec_rl_lab.tasks` module registers all **arena–robot combinations** as Gym-compatible environments, enabling unified interfaces for evaluation and submission. |
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| | Arena \ Robot | G1 | Tron1Piper | Tron2ALegged | Tron2AWheel | B2Piper | B2wPiper | Piper | |
| | ------------- | --------------- | ----------------------- | ------------------------- | ------------------------ | -------------------- | --------------------- | ------------------ | |
| | Task A | `ATEC-TaskA-G1` | `ATEC-TaskA-Tron1Piper` | `ATEC-TaskA-Tron2ALegged` | `ATEC-TaskA-Tron2AWheel` | `ATEC-TaskA-B2Piper` | `ATEC-TaskA-B2wPiper` | | |
| | Task B | `ATEC-TaskB-G1` | `ATEC-TaskB-Tron1Piper` | `ATEC-TaskB-Tron2ALegged` | `ATEC-TaskB-Tron2AWheel` | `ATEC-TaskB-B2Piper` | `ATEC-TaskB-B2wPiper` | | |
| | Task D | `ATEC-TaskD-G1` | `ATEC-TaskD-Tron1Piper` | `ATEC-TaskD-Tron2ALegged` | `ATEC-TaskD-Tron2AWheel` | `ATEC-TaskD-B2Piper` | `ATEC-TaskD-B2wPiper` | | |
| | Task E | | | | | | | `ATEC-TaskE-Piper` | |
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| > **Note:** The provided environments are designed for evaluation and submission only and do not support parallelized training. For training, users should implement custom wrappers or leverage external frameworks for efficient learning. |
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| --- |
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| ## 2. Installation |
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| This repository is developed and tested with **Isaac Lab v2.3.2**. Earlier versions (e.g., v1.4.1) are not validated and may require modification. |
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| Follow the official Isaac Lab installation [guide](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/pip_installation.html). |
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| ### 2.1 Setup |
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| Clone repository |
| ```bash |
| git clone https://github.com/atecup/ATEC2026_Simulation_Challenge.git |
| cd ATEC2026_Simulation_Challenge |
| ``` |
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| Activate Isaac Lab Environment |
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| ```bash |
| conda activate isaaclab |
| ``` |
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| Install ATEC Extension |
| ```bash |
| cd source/atec_rl_lab |
| pip install -e . |
| ``` |
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| After installation, all `ATEC-*` environments will be available in the active Python environment. |
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| Download Robot Models |
| ```bash |
| cd ATEC2026_Simulation_Challenge |
| curl https://static.atecup.com/atec2026/atec_robot_model.zip -o atec_robot_model.zip |
| unzip atec_robot_model.zip -d atec_robot_model |
| ``` |
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| --- |
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| ## 3. Running the Environments |
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| ### 3.1 Environment Check |
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| ```bash |
| cd ATEC2026_Simulation_Challenge |
| python scripts/list_envs.py |
| ``` |
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| Successful execution will list all registered environments, confirming correct module loading. |
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| --- |
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| ### 3.2 Visualization Utilities |
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| ```bash |
| scripts/view_robots.py – inspect robot models |
| scripts/view_task_a.py – Task A visualization |
| scripts/view_task_b.py – Task B visualization |
| scripts/view_task_d.py – Task D visualization |
| scripts/view_task_e.py – Task E visualization |
| ``` |
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| Example: |
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| ``` |
| python scripts/view_task_a.py --enable_cameras |
| ``` |
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| --- |
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| ### 3.3 Submission and Evaluation |
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| Participants can test their solutions using: |
| ```bash |
| cd ATEC2026_Simulation_Challenge |
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| python scripts/play_atec_task.py --task ATEC-TaskA-G1 --enable_cameras |
| ``` |
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| #### Implementation Requirement |
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| Participants must implement demo/solution.py, and this file name can not be changed. |
| * Class: AlgSolution |
| * Function: predicts(obs, current_score), where **obs** is the observation, and **current_score** is the current score |
| * Return: {"action": action, "giveup": False}, where action is the prediction action represented by List, and **giveup** is the giveup flag. if **giveup** is True, the scoring job will be terminated. |
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| ### 3.4 Observations and Actions |
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| #### Tasks A / B / D |
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| Observations are grouped into: |
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| - `Proprioception`: base velocity, joint states, previous actions |
| - `Exteroception`: LiDAR-based height scan |
| - `Vision`: RGB-D images from head and end-effector cameras |
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| All observation terms are: |
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| - noise-injected |
| - order-preserved |
| - concatenated per group |
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| #### Task E (Manipulation-only) |
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| Observations include: |
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| - `Proprioception`: joint states (position + velocity) |
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| - `Vision`: RGB-D images from end-effector and external camera |
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| **Note:** Joint indices follow fixed ordering per robot (critical for policy deployment). |
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| - b2_piper (20 DoF) |
| - b2w_piper (24 DoF) |
| - G1 (33 DoF) |
| - tron1a_piper (16 DoF) |
| - piper (8 DoF) |
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| #### Action Space |
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| Robot control actions are organized by joint type. |
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| - Leg joints and manipulator joints are controlled by joint position commands. |
| - Wheel joints of wheeled robots are controlled by joint velocity commands. |
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| The action configuration is as follows: |
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| ``` |
| joint_pos_leg = mdp.JointPositionActionCfg( |
| asset_name="robot", |
| joint_names=[""], |
| scale=0.5, |
| use_default_offset=True, |
| clip=None, |
| preserve_order=True, |
| ) |
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| joint_vel_wheel = mdp.JointVelocityActionCfg( |
| asset_name="robot", |
| joint_names=[""], |
| scale=5.0, |
| use_default_offset=True, |
| clip=None, |
| preserve_order=True, |
| ) |
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| joint_pos_arm = mdp.JointPositionActionCfg( |
| asset_name="robot", |
| joint_names=[""], |
| scale=0.5, |
| use_default_offset=True, |
| clip=None, |
| preserve_order=True, |
| ) |
| ``` |
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| ##### Scaling rules |
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| - Leg position commands are scaled by 0.5 before being applied to the robot. |
| - Arm position commands are scaled by 0.5 before being applied to the robot. |
| - Wheel velocity commands are scaled by 5.0 before being applied to the robot. |
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| Different robots enable different action items according to their structure: |
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| - Standard legged robots |
| (humanoid robots, quadruped mobile manipulator robots, manipulator) do not enable wheel velocity control. |
| - Wheeled legged robots |
| (Dual-wheel legged mobile manipulator robots, quadruped-wheel legged mobile manipulator robots) |
| enable wheel velocity control. |
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| ## Contributors |
| - **[CUHK Legged Robot Lab](https://cuhkleggedrobotlab.github.io/)** |
| - **[曾兆阳](https://zengzhaoyang.com/)** |
| - **[ATEC (Advanced Technology Exploration Community)](https://www.atecup.com)** |
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| ## License |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
