Welcome to the ISAE-SUPAERO SDD HACKATHON 2026 repository 🎉
Here you’ll find everything you need to get started: objectives, rules, tips, and resources to make the most out of this exciting challenge!
📚 Table of contents
The challenge: teach a robot arm to place 1, 2, or 3 colored cubes onto a 2×2 wooden grid. Each cube should be placed in the cell of the corresponding colored cross.
Sounds simple? In practice, it’s a tough robotics + learning task! Success depends on how strategically you build datasets (coverage & curriculum), train models, and plan your approach.
Your robot will be evaluated over 4 levels of increasing difficulty. Each level has 5 trials. You must succeed on at least 3/5 trials to unlock the next level.
Level 1 — Single Color, Fixed Position
Task: Pick up one cube of a single color and place it on the same target cell every time (as indicated by the card) in ≤ 20s. Scoring: 5 points per success (+5 bonus if the cube is grasped on the first attempt).
Level 2 — Single Cube, Varying Positions
Task: Pick up one cube and place it on the correct cell (position varies across trials) in ≤ 20s. Scoring: 10 points per success (+5 bonus for first-attempt grasps).
Level 3 — Two Colored Cubes, Varying Positions
Task: Pick and place 2 cubes of different colors on their respective target cells in ≤ 30s. Scoring: 30 points per success.
Level 4 — Three Colored Cubes, Varying Positions
Task: Pick and place 3 cubes of different colors on their respective target cells in ≤ 30s. Scoring: 50 points per success.
Warning
Day 1
09:00: 🟢 Official start!
11:30 Run first training tests to verify dataset format, logging, and checkpoints.
14:00 Test inference from early checkpoints.
17:30: First evaluation attempt (even with partial training).
Day 2
09:00: Evaluation with overnight training results on first levels.
17:30: Preliminary evaluation on more difficult levels.
Day 3
09:00 Evaluation with overnight training results on more difficult levels.
11:00 Final evaluations & videos for wrap-up presentations.
12:00 Pizza time 🍕
14:00-16:00: Preparation of team presentations (strategies, results, lessons learned). No more access to the robot setups.
16:00-17:00: Final wrap-up session of the hackathon.
Plan your recording setup: Each team gets 2 external cameras. Place them wisely (wrist, top, side view, etc.). Name them clearly when recording datasets.
Record plenty of data: Rotate who records to get variety. Consistency matters!
Mark positions: If you set up a controlled environment, mark object positions (objects may be moved overnight).
Robustness vs. simplicity:
- Controlled environment = easier early progress.
- Varied environments = robustness but needs more data.
- Curriculum strategy: Start with lots of Level 1 data, then gradually add Level 2, Level 3, etc. However, this is not an obligation. If you and your teammates want to win all or nothing we respect that!
Note
💡 Check out the dataset guidelines for more details.
- Think strategically: scoring is incremental, don’t rush for Level 4.
- Save checkpoints and test early & often.
- Teamwork matters as much as models: rotate recording, share insights.
- Remember: the goal is to learn, experiment, and have fun 🎉
Two environments should be set:
- one on your local machine for data recording and inference,
- a second one on a remote GPU machine for model training.
Follow the setup instructions for both systems in setup_instructions.md.
When the setup is ready on your local machine, you can start recording episodes for your dataset. Follow the instructions and tips in data_recording.md.
Once data is recorded, you can train a model on the remote machine. We provide a toy dataset to try a training stage without waiting for your own data recording. You can save time to ensure that your remote machine is correctly set up and ready for training.
The toy dataset can be found here on Hugging Face
and can be used in the training command line using argument
--dataset.repo_id=DEEL-AI/Hackathon_Team0Z.
All instructions and tips for training are given in training.md.
To run inference on your local machine, you first need to get your trained checkpoints. If you want to get them from the remote machine, you can use scp:
scp -r username@remote_machine://output_dir/train/act_so101_test/checkpoints/last C:\path\to\outputs\train\act_so101_test\checkpointsThen, using your local environment setup, you can run:
lerobot-record \
--robot.type=so101_follower \
--robot.port='COM5' \
--robot.id=follower_f0 \
--robot.calibration_dir="path\to\lerobot-hackathon\calibration\robots\so101_follower" \
--robot.cameras="{ left: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}}" \ # <-- Change with your setting
--teleop.type=so101_leader \
--teleop.port='COM4' \
--teleop.id=leader_l0 \
--teleop.calibration_dir="path\to\lerobot-hackathon\calibration\teleoperators\so101_leader" \
--display_data=true \
--dataset.single_task="Pick and place one green cube on the cell indicated by the card on a 2×2 grid." \
--dataset.root='./eval_hackathon_9_cubes_v1' \
--dataset.repo_id='DEEL-AI/eval_Hackathon_TeamXX' \
--dataset.push_to_hub=false \
--policy.path="C:\path\to\outputs\train\act_so101_test\checkpoints\last\pretrained_model"You don’t need to include the teleop arguments if you prefer not to. However, adding them allows you to press ← (left arrow) during inference to temporarily take manual control of the robot and reset it, before pressing → (right arrow) to continue to the next episode.
Note
You can modify the --dataset.single_task flag to change the command prompt. That said, we recommend using the exact same commands as those in your dataset to ensure consistency.
The keyboard shortcuts behave the same way as during recording, except you won’t need to teleoperate—the robot will autonomously execute episodes. For testing, we provide a sample policy that you can run before training your own. Its behavior may be erratic, but it’s useful for verifying that inference is working correctly.
✅ You are now fully set up and ready to record, train, and run inference!