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Datasets:
pbelevich
/
lego42176_garage_parking

Tasks:
Robotics
Modalities:
Tabular
Time-series
Video
Formats:
parquet
Size:
10K - 100K
Tags:
LeRobot
act
imitation-learning
robotics
physical-ai
lego
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Polars
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LEGO 42176 Garage Parking Dataset

Imitation learning dataset for driving a LEGO Technic 42176 car into a garage. Recorded with LeRobot v3.0 format.

A human operator drives the car via keyboard over Bluetooth while an IP camera records the scene. The policy learns to map camera observations to speed/steering commands.

Dataset summary

Episodes 100
Total frames 12,037
Duration 13.4 minutes
FPS 15
Resolution 640 x 480
Robot type lego42176
Task Drive the LEGO car into the garage

Episode statistics

Min Median Mean Max
Frames 48 113 120 260
Duration (s) 3.2 7.5 8.0 17.3

Features

Feature Type Shape Description
observation.image video (h264) [480, 640, 3] Camera view of the car and garage
observation.state float32 [2] Current [speed, steering] normalized to [-1, 1]
action float32 [2] Commanded [speed, steering] normalized to [-1, 1]

Action space

Actions are continuous values in [-0.8, 0.8] (normalized from the LEGO hub's [-80, 80] range):

  • speed: positive = forward, negative = reverse
  • steering: positive = right, negative = left

How it was recorded

  1. LEGO 42176 car connected via Bluetooth Low Energy (BLE)
  2. IP camera streaming RTSP at 640x480
  3. Human drives the car from a starting position into a garage using keyboard (arrow keys)
  4. Each episode = one parking attempt (press R to start, drive, press R to save)
  5. Dataset finalized after each episode to prevent data loss

Usage 

from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("pbelevich/lego42176_garage_parking")

sample = dataset[0]
print(sample["observation.image"].shape)  # torch.Size([3, 480, 640])
print(sample["action"].shape)             # torch.Size([2])

Training

This dataset was used to train an ACT (Action Chunking with Transformers) policy on Apple Silicon (MPS backend). See the project repository for training and inference scripts.

Hardware

  • Car: LEGO Technic 42176 Porsche GT4 e-Performance
  • Camera: Dahua IP camera, 640x480 via RTSP (substream)
  • Compute: MacBook Pro M1 Pro
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