LeRobot v0.4.0: Supercharging OSS Robot Learning

LeRobot v0.6.0: Imagine, Evaluate, Improve

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

!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.

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

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

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

A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning

The paper presents a decentralized navigation framework for composite heterogeneous robots that integrates a large language model (LLM) policy agent, an Upper Confidence Bound (UCB) bandit, and a Double Deep Q-Network (Double DQN) controller. Each robot independently generates and refines policies at the round level using LLM inference, while the Double DQN handles tick-level action selection based on navigation variables and LLM priors. Across 30 rounds, the full configuration achieved all goals with the lowest median completion time (42 ticks) and a 25–39% improvement over other setups.

ASPIRE: Agentic /Skills Discovery for Robotics

arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.

Teach and Grow: An Agent-Centered Architecture for General Robot Learning

Teach-and-Grow Learning (TGL) is an agent-centered architecture that transforms a few successful demonstrations into reusable Skill Blocks, enabling a robot to compose, execute, and revise behaviors in new scenes without task-specific policy retraining. The system maintains a Skill Library and structured Experience Memory to capture successes, failures, and repairs, allowing persistent reuse and agent-directed adaptation. Evaluation on the LIBERO benchmark shows state-of-the-art performance, and the authors propose a scaling-law hypothesis suggesting that accumulated reusable experience reduces future-task error and teaching demand following a power-law trend.

G0.5: One Autoregressive Stream for Robot Reasoning and Action

arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.

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

Robots that learn

We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.