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We present RoboGen, a generative robotic agent that automatically learnsdiverse robotic skills at scale via generative simulation. RoboGen leveragesthe latest advancements in foundation and generative models. Instead ofdirectly using or adapting these models to produce policies or low-levelactions, we advocate for a generative scheme, which uses these models toautomatically generate diversified tasks, scenes, and training supervisions,thereby scaling up robotic skill learning with minimal human supervision. Ourapproach equips a robotic agent with a self-guided propose-generate-learncycle: the agent first proposes interesting tasks and skills to develop, andthen generates corresponding simulation environments by populating pertinentobjects and assets with proper spatial configurations. Afterwards, the agentdecomposes the proposed high-level task into sub-tasks, selects the optimallearning approach (reinforcement learning, motion planning, or trajectoryoptimization), generates required training supervision, and then learnspolicies to acquire the proposed skill. Our work attempts to extract theextensive and versatile knowledge embedded in large-scale models and transferthem to the field of robotics. Our fully generative pipeline can be queriedrepeatedly, producing an endless stream of skill demonstrations associated withdiverse tasks and environments.

RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation