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

SmolVLA: Efficient Vision-Language-Action Model trained on Lerobot Community Data

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

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

LeRobot v0.4.0: Supercharging OSS Robot Learning

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

!Imperio, smolVLA: The Implications of Data Poisoning on Open Source Robotics

arXiv:2607. 04146v1 Announce Type: cross Abstract: This work establishes that trigger-word data poisoning of vision language action models is practical, while at the same time the open-source robotics ecosystem holds trust assumptions about community contributions.

LeRobot v0.6.0: Imagine, Evaluate, Improve

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

arXiv:2606. 14409v1 Announce Type: cross Abstract: In this report, we present Hy-Embodied-0.

Scaling robotics datasets with video encoding

Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data

arXiv:2606. 08107v1 Announce Type: cross Abstract: Robotics faces a fundamental challenge of data scarcity.

HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos

HumanEgo is a framework that enables zero‑shot robot learning from short egocentric human videos by converting each demonstration into an entity‑level hand‑object interaction representation and training a flow‑matching policy with dense auxiliary objectives. The method is robot‑data‑free, hardware‑agnostic, and data‑efficient, achieving 92.5 % success on four real‑world tasks with only 30 minutes of human video per task and outperforming matched‑time robot teleoperation by 41 %. HumanEgo also robustly transfers zero‑shot across new robots, cameras, and environments, and is released as an open‑source tool for learning robot policies directly from human data.

GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks

arXiv:2607. 05369v1 Announce Type: cross Abstract: For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world adaptability of model-free policies?