NVIDIA and Hugging Face are releasing the NVIDIA Isaac GR00T 1.7 model and Isaac Teleop framework into LeRobot, an open-source robotics library, to provide developers with shared tools for training, evaluating, and deploying robot foundation models. NVIDIA Cosmos 3, a frontier world model for physical AI, is also set to be integrated soon.
Editorial context：NVIDIA and Hugging Face are collaborating to integrate advanced models and frameworks into LeRobot, an open-source robotics library, to streamline end-to-end robot development and foster community innovation.
This tutorial explores the challenges of deploying VLA models on embedded robotic systems, including dataset recording best practices, fine-tuning techniques for ACT and SmolVLA, and real-time performance optimization using the NXP i.MX 95 SoC. It emphasizes asynchronous inference and hardware-aware scheduling to improve control and reduce latency.
Editorial context：This guide provides hands-on best practices for deploying Vision-Language-Action (VLA) models on embedded platforms, emphasizing dataset recording, model fine-tuning, and real-time performance optimization. It highlights the importance of asynchronous inference and hardware-specific optimizations for achieving reliable robotic control.
LeRobot v0.4.2 includes bug fixes, performance improvements, and new features like real-time chunking and dataset encoding optimizations. Updates address episode filtering, script porting, and environment preprocessing.
AMD, Hugging Face, and Data Monsters host a robotics hackathon with in-person events in Tokyo and Paris. Teams compete with LeRobot kits and AMD hardware.
This hands-on tutorial walks through the process of collecting data, training policies, and deploying autonomous medical robotics workflows on real hardware using NVIDIA Isaac for Healthcare. It introduces the SO-ARM starter workflow, which enables developers to build and validate surgical assistant robots from simulation to deployment.
Editorial context：This tutorial provides a comprehensive guide to building a healthcare robot using NVIDIA Isaac for Healthcare, covering data collection, simulation, training, and deployment on real hardware. It emphasizes the use of simulation to generate synthetic data and the integration of real-world data for training policies that generalize across domains.
Hugging Face announces LeRobot v0.4.0, a major upgrade for open-source robotics with Dataset v3.0, new VLA models like PI0.5 and GR00T N1.5, and a plugin system for hardware integration. The release also adds support for LIBERO and Meta-World simulations, multi-GPU training, and a new Hugging Face Robot Learning Course.
Editorial context：LeRobot v0.4.0 introduces significant upgrades for open-source robotics, including Dataset v3.0 with chunked episodes and streaming capabilities, new VLA models like PI0.5 and GR00T N1.5, and a plugin system for hardware integration. These enhancements improve scalability, data management, and support for simulation environments like LIBERO and Meta-World.
Open Robotics reports record nine students participating in GSoC 2025 across five projects. Key projects include ROS 2 tracing improvements, C/C++ to ROS message conversion, and Gazebo-based sonar simulations.
NVIDIA has released the GR00T N1.5 model, a cross-embodiment foundation model for generalized humanoid robot reasoning and skills. The model can be fine-tuned using teleoperation data from a SO-101 arm, with a detailed tutorial provided for developers. The release includes instructions for dataset preparation, fine-tuning, evaluation, and deployment.
Editorial context：NVIDIA's GR00T N1.5 is a cross-embodiment model for generalized humanoid robot reasoning and skills, adaptable through post-training for specific tasks and environments. The release includes a step-by-step tutorial for fine-tuning using teleoperation data from a SO-101 arm, emphasizing the use of the EmbodimentTag system for customization.
Hugging Face introduces SmolVLA, a 450M parameter open-source Vision-Language-Action model for robotics, trained on community-shared datasets. It outperforms larger models in simulation and real-world tasks, supports asynchronous inference for faster response, and is designed for deployment on consumer hardware.
Editorial context：SmolVLA demonstrates that compact, open-source models can outperform larger proprietary systems in both simulation and real-world tasks, highlighting the potential of community-driven data and efficient architectures in advancing robotics research.