RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning
  via Generative Simulation
Authors: name: Yufei Wang, name: Zhou Xian, name: Feng Chen, name: Tsun-Hsuan Wang, name: Yian Wang, name: Katerina Fragkiadaki, name: Zackory Erickson, name: David Held, name: Chuang Gan

We present RoboGen, a generative robotic agent that automatically learns
diverse robotic skills at scale via generative simulation. RoboGen leverages
the latest advancements in foundation and generative models. Instead of
directly using or adapting these models to produce policies or low-level
actions, we advocate for a generative scheme, which uses these models to
automatically generate diversified tasks, scenes, and training supervisions,
thereby scaling up robotic skill learning with minimal human supervision. Our
approach equips a robotic agent with a self-guided propose-generate-learn
cycle: the agent first proposes interesting tasks and skills to develop, and
then generates corresponding simulation environments by populating pertinent
objects and assets with proper spatial configurations. Afterwards, the agent
decomposes the proposed high-level task into sub-tasks, selects the optimal
learning approach (reinforcement learning, motion planning, or trajectory
optimization), generates required training supervision, and then learns
policies to acquire the proposed skill. Our work attempts to extract the
extensive and versatile knowledge embedded in large-scale models and transfer
them to the field of robotics. Our fully generative pipeline can be queried
repeatedly, producing an endless stream of skill demonstrations associated with
diverse tasks and environments.