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

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

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

LeRobot v0.6.0: Imagine, Evaluate, Improve

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

LeRobot v0.4.0: Supercharging OSS Robot Learning

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

CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?

Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development. Such environments typically involve both complex code and large-scale data (i.

!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.

CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?

arXiv:2606. 15300v1 Announce Type: new Abstract: Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of real-world development.

SciDER: Scientific Data-centric End-to-end Researcher

arXiv:2603. 01421v3 Announce Type: replace Abstract: While large language models accelerate scientific discovery, existing agents face severe limitations in adaptability, domain generalization, and multimodal scalability, often struggling to autonomously process raw, domain-specific experimental data.

Libra: Training the Environment for Agentic Information Retrieval

arXiv:2607. 00016v1 Announce Type: cross Abstract: Information localization within massive repositories is a cornerstone of agentic LLM systems.

Modeling Robotics Dataset Construction as an Artifact-Based Build Process

arXiv:2606. 00162v1 Announce Type: cross Abstract: Robotic systems generate large volumes of multimodal sensor data, but converting ROS bag recordings into machine learning datasets is often handled by ad hoc sequential scripts, creating engineering overhead and slow iteration cycles.