Preface: The goal of generating synthetic data for robot models is to create a diverse and realistic dataset for training and validating AI systems in a cost-effective and scalable way, helping to overcome the limitations of real-world data collection. This includes creating data for training models, improving their performance, testing for edge cases, and refining them after initial training without needing extensive, time-consuming, or dangerous physical data collection.
Background: Isaac Sim facilitates three essential workflows: generating synthetic data for training or post-training robot models used for perception, mobility, and manipulation. It also enables validating robot stacks through software and hardware-in-loop testing and enabling robot learning through Isaac™ Lab.
NVIDIA Isaac Lab is an open-source, unified framework for robot learning that helps developers train robot policies using high-fidelity simulation. Built on NVIDIA Isaac Sim and the Omniverse platform, it leverages the power of GPUs for parallel physics simulation and photorealistic rendering to bridge the gap between simulation and real-world training. The framework simplifies common workflows for robot learning, such as reinforcement learning and imitation learning, by providing modular design patterns and a unified set of tools.
Configuring Stable-Baselines3 (SB3) within Isaac Sim, particularly with Isaac Lab, involves setting up the training environment and specifying hyperparameters for your chosen reinforcement learning algorithm.
Vulnerability details: NVIDIA Isaac Lab contains a vulnerability in SB3 configuration parsing. A successful exploit of this vulnerability might lead to code execution, denial of service, escalation of privileges, information disclosure, or data tampering.
Official announcement: Please see the link for details