Isaac Lab is an open-source, GPU-accelerated robotics learning framework built on NVIDIA Isaac Sim. It provides high-fidelity physics and sensor simulation, ready-to-train environments and robot models, and integrations for reinforcement and imitation learning workflows to accelerate sim-to-real research and large-scale robot training.
This paper proposes a quantitative framework for the rise-and-fall trajectory of complexity in closed systems, showing that a coffee-and-cream cellular automaton exhibits a bell-curve of apparent complexity when particles interact, thereby linking information theory with thermodynamics and self-organization.
