Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models
Pushkal Katara Zhou Xian Katerina Fragkiadaki
Generalist robot manipulators need to learn a wide variety of manipulation
skills across diverse environments. Current robot training pipelines rely on
humans to provide kinesthetic demonstrations or to program simulation
environments and to code up reward functions for reinforcement learning. Such
human involvement is an important bottleneck towards scaling up robot learning
across diverse tasks and environments. We propose Generation to Simulation
(Gen2Sim), a method for scaling up robot skill learning in simulation by
automating generation of 3D assets, task descriptions, task decompositions and
reward functions using large pre-trained generative models of language and
vision. We generate 3D assets for simulation by lifting open-world 2D
object-centric images to 3D using image diffusion models and querying LLMs to
determine plausible physics parameters. Given URDF files of generated and
human-developed assets, we chain-of-thought prompt LLMs to map these to
relevant task descriptions, temporal decompositions, and corresponding python
reward functions for reinforcement learning. We show Gen2Sim succeeds in
learning policies for diverse long horizon tasks, where reinforcement learning
with non temporally decomposed reward functions fails. Gen2Sim provides a
viable path for scaling up reinforcement learning for robot manipulators in
simulation, both by diversifying and expanding task and environment
development, and by facilitating the discovery of reinforcement-learned
behaviors through temporal task decomposition in RL. Our work contributes
hundreds of simulated assets, tasks and demonstrations, taking a step towards
fully autonomous robotic manipulation skill acquisition in simulation.