LeRobot v0.5.0: Scaling Every Dimension

LeRobot v0.6.0: Imagine, Evaluate, Improve

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

LeRobot v0.4.0: Supercharging OSS Robot Learning

Scaling robotics datasets with video encoding

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

Finer is Better (with the Right Scaling)

arXiv:2605. 08565v2 Announce Type: replace Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats.

Post-Training Isaac GR00T N1.5 for LeRobot SO-101 Arm

Small-Scale Experiments: Are We There Yet?

arXiv:2608. 11859v1 Announce Type: new Abstract: Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver.

SmolVLM Grows Smaller – Introducing the 256M & 500M Models!

Small-Scale Experiments: Are We There Yet?

Scaling laws promised cost-effective experiments; six years later, they have yet to fully deliver. Instead, researchers have found them unreliable at small scales (starting at 4M parameters) and concluded that sizable models cannot be avoided.

The Hidden Power of Scaling Factor in LoRA Optimization

arXiv:2606. 12883v1 Announce Type: new Abstract: In Low-Rank Adaptation (LoRA), the scaling factor $\alpha$ is often treated as a mere complement to the learning rate, yet its role in optimization remains poorly understood.

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