IRASim: Learning Interactive Real-Robot Action Simulators
Authors: name: Fangqi Zhu, name: Hongtao Wu, name: Song Guo, name: Yuxiao Liu, name: Chilam Cheang, name: Tao Kong

Scalable robot learning in the real world is limited by the cost and safety
issues of real robots. In addition, rolling out robot trajectories in the real
world can be time-consuming and labor-intensive. In this paper, we propose to
learn an interactive real-robot action simulator as an alternative. We
introduce a novel method, IRASim, which leverages the power of generative
models to generate extremely realistic videos of a robot arm that executes a
given action trajectory, starting from an initial given frame. To validate the
effectiveness of our method, we create a new benchmark, IRASim Benchmark, based
on three real-robot datasets and perform extensive experiments on the
benchmark. Results show that IRASim outperforms all the baseline methods and is
more preferable in human evaluations. We hope that IRASim can serve as an
effective and scalable approach to enhance robot learning in the real world. To
promote research for generative real-robot action simulators, we open-source
code, benchmark, and checkpoints at https: //gen-irasim.github.io.