7 articles
OpenAI develops a hierarchical reinforcement learning algorithm that learns reusable high-level actions. Applied to navigation, the agent discovers primitives (walking, crawling) and rapidly solves tasks requiring thousands of timesteps.
OpenAI presents robotics techniques enabling robot controllers trained entirely in simulation to adapt to unexpected environmental changes during physical deployment. Shift from open-loop to closed-loop systems.
OpenAI demonstrates sim-to-real transfer of robotic control using dynamics randomization. A robot trained in simulation with varied physical parameters (friction, mass, latency) successfully performs complex tasks on real hardware without additional tuning.
OpenAI releases an asymmetric actor-critic method for robot learning: a lightweight actor runs on-robot while a heavy critic trains offline. Approach tested on object manipulation tasks.
OpenAI combines domain randomization and generative models to improve robotic grasping. The approach trains models on varied synthetic data, then transfers them to real robots without additional fine-tuning.
OpenAI demonstrates that a meta-learning agent can quickly learn to defeat a stronger non-meta-learning opponent in simulated robot wrestling and adapt to physical malfunctions.
OpenAI shows self-play enables AIs to discover complex physical skills (tackling, ducking, faking, kicking, catching) without explicit environment design. The environment automatically adapts to optimal difficulty. Results replicated on Dota 2.