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Gen2Sim Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models Accepted at ICRA 2024 Pushkal Katara* 1 , Zhou Xian* 1 , Katerina Fragkiadaki 1 1 Carnegie Mellon University Corresponding author: pkatara@andrew.cmu.edu arXiv Code Abstract 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 ...
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