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atec2026-task-e-reproducibility

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.

This repository includes simulation assets, task definitions, and reference scripts to support development, evaluation, and submission.

1.2 Robots and Sensors

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)
Robot Platforms

Humanoid Dual-wheel legged + manipulator Tron2A legged + manipulator Tron2A wheel + manipulator Quadruped + manipulator Wheel-legged quadruped + manipulator Manipulator

Note: Users may modify or optimize assets (e.g., collision geometry simplification) for training purposes. The provided assets serve as reference models for evaluation.

1.3 Challenge Arenas

Arena	Video	Arena	Video
Task A · Off-road Navigation		Task E · Tabletop Manipulation
Task B · Garbage Collection		Task D · Obstacle Traversal

Note: For each task, participants are free to select any supported robot morphology.

1.4 Environment Matrix

The atec_rl_lab.tasks module registers all arena–robot combinations as Gym-compatible environments, enabling unified interfaces for evaluation and submission.

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`

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.

2. Installation

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.

Follow the official Isaac Lab installation guide.

2.1 Setup

Clone repository

git clone https://github.com/atecup/ATEC2026_Simulation_Challenge.git
cd ATEC2026_Simulation_Challenge

Activate Isaac Lab Environment

conda activate isaaclab

Install ATEC Extension

cd source/atec_rl_lab
pip install -e .

After installation, all ATEC-* environments will be available in the active Python environment.

Download Robot Models

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

3. Running the Environments

3.1 Environment Check

cd ATEC2026_Simulation_Challenge
python scripts/list_envs.py

Successful execution will list all registered environments, confirming correct module loading.

3.2 Visualization Utilities

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

Example:

python scripts/view_task_a.py --enable_cameras

3.3 Submission and Evaluation

Participants can test their solutions using:

cd ATEC2026_Simulation_Challenge

python scripts/play_atec_task.py --task ATEC-TaskA-G1 --enable_cameras

Implementation Requirement

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.

3.4 Observations and Actions

Tasks A / B / D

Observations are grouped into:

Proprioception: base velocity, joint states, previous actions
Exteroception: LiDAR-based height scan
Vision: RGB-D images from head and end-effector cameras

All observation terms are:

noise-injected
order-preserved
concatenated per group

Task E (Manipulation-only)

Observations include:

Proprioception: joint states (position + velocity)
Vision: RGB-D images from end-effector and external camera

Note: Joint indices follow fixed ordering per robot (critical for policy deployment).

b2_piper (20 DoF)
b2w_piper (24 DoF)
G1 (33 DoF)
tron1a_piper (16 DoF)
piper (8 DoF)

Action Space

Robot control actions are organized by joint type.

Leg joints and manipulator joints are controlled by joint position commands.
Wheel joints of wheeled robots are controlled by joint velocity commands.

The action configuration is as follows:

joint_pos_leg = mdp.JointPositionActionCfg(
    asset_name="robot",
    joint_names=[""],
    scale=0.5,
    use_default_offset=True,
    clip=None,
    preserve_order=True,
)

joint_vel_wheel = mdp.JointVelocityActionCfg(
    asset_name="robot",
    joint_names=[""],
    scale=5.0,
    use_default_offset=True,
    clip=None,
    preserve_order=True,
)

joint_pos_arm = mdp.JointPositionActionCfg(
    asset_name="robot",
    joint_names=[""],
    scale=0.5,
    use_default_offset=True,
    clip=None,
    preserve_order=True,
)

Scaling rules

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.

Different robots enable different action items according to their structure:

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.

Contributors

CUHK Legged Robot Lab
曾兆阳
ATEC (Advanced Technology Exploration Community)

License

This project is licensed under the MIT License - see the LICENSE file for details.

1. Introduction