LeRobot goes to driving school: World’s largest open-source self-driving dataset

`LeRobotDataset:v3.0`: Bringing large-scale datasets to `lerobot`

LeRobot Community Datasets: The “ImageNet” of Robotics — When and How?

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations

LeRobot v0.4.0: Supercharging OSS Robot Learning

Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark

arXiv:2607. 00710v1 Announce Type: cross Abstract: Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategically design impactful ones.

TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving

arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.

SSIL: Self-Supervised Imitation Learning for End-to-End Driving

arXiv:2308. 14329v4 Announce Type: replace-cross Abstract: In autonomous driving, the end-to-end (E2E) driving approach that predicts vehicle control signals directly from sensor data is rapidly gaining attention.

LeRobot v0.6.0: Imagine, Evaluate, Improve

Pictura: Perspective-View Self-Play at Scale for Driving

arXiv:2607. 26005v1 Announce Type: cross Abstract: Self-play in simulation produces robust driving policies at scale.

The LAIA Dataset: Labelled Attention for Intelligent Automobiles

arXiv:2607. 25570v1 Announce Type: cross Abstract: The development of autonomous vehicles (AVs) usually relies heavily on data-driven artificial intelligence (AI) models that require large volumes of sensor data with ground-truth annotations.

Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders

Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment

arXiv:2608. 12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments.