example.md

1. Example Code Guide

This document provides a minimal end-to-end example for training, evaluating, and submitting a policy.

2. RL for Locomotion

2.1 Train a PPO Policy (Example)

The baseline workflow references: https://github.com/fan-ziqi/robot_lab

Run the following command from the repository root:

python scripts/rsl_rl/train.py --task ATEC-Isaac-Velocity-Flat-Unitree-B2-v0 --headless --video

On an NVIDIA RTX 5090, this example typically takes around 90 minutes.

Actual training time depends on driver/runtime version, CPU performance, and current GPU load.

2.2 Evaluate the Trained Policy

After training, evaluate with:

python scripts/rsl_rl/play.py --task ATEC-Isaac-Velocity-Flat-Unitree-B2-v0

This loads the trained checkpoint and runs rollout in the same task setting.

2.3 Test Locally

The file demo/solution.py is the only entrance for locally testing and online submission. Use the test command:

cd ATEC2026_Simulation_Challenge
python scripts/play_atec_task.py --task ATEC-TaskA-B2Piper --enable_cameras

Notes:

• --task selects the arena and robot. See the Environment Matrix in readme.md.
• Use --debug to print runtime status and score.

Pretrained baseline checkpoint:

• ./atec_robot_model/baseline/unitree_b2_flat/policy.pt This checkpoint path can be modified in demo/solution.py.

3. IL for Manipoulation

This section follows the core idea of ACT (Action Chunking with Transformers).

Reference implementation: https://github.com/tonyzhaozh/act

3.1 Collect Demonstrations

Collect expert trajectories for Task E:

python scripts/act/collect_demos_task_e.py --pick_objects 3 --num_demos 100 --headless --enable_cameras --save_images

Filter out near-zero actions from the collected dataset:

python scripts/act/filter_demos.py \
    --input datasets/atec_task_e/trajectory.hdf5 \
    --output datasets/atec_task_e/trajectory_filtered.hdf5 \
    --threshold 0.001

3.2 Train ACT Policy

Run ACT baseline training from the scripts/act directory:

cd scripts/act
bash baseline.sh

3.3 Run the Trained Policy

Use the test command:

python scripts/play_atec_task.py --task ATEC-TaskE-Piper --enable_cameras

Note: ./atec_robot_model/baseline/act/policy.pt is the provided baseline checkpoint. You can replace it with your own trained policy path in demo/solution.