IRASim: Learning Interactive Real-Robot Action Simulators
Fangqi Zhu, Hongtao Wu, Song Guo, Yuxiao Liu, Chilam Cheang, Tao Kong
2024-06-25
Summary
This paper introduces IRASim, a new system designed to create realistic simulations of robots performing tasks. It helps robots learn and practice their actions in a safe and efficient way without the need for physical trials.
What's the problem?
Training robots in the real world can be expensive and risky. It often takes a lot of time and effort to set up and execute robot movements, which makes it difficult to teach them new skills effectively. This limits how quickly and safely robots can learn to perform tasks.
What's the solution?
The authors developed IRASim, which uses advanced generative models to create high-quality videos of a robot arm executing specific actions based on given trajectories. This means that instead of physically moving the robot, they can simulate its actions in a virtual environment. They also created a benchmark called the IRASim Benchmark to test and validate how well their simulator works compared to other methods. Their results show that IRASim performs better than existing approaches and is preferred in evaluations by people.
Why it matters?
This research is important because it provides a new way for robots to learn without the risks and costs associated with real-world training. By using simulations, robots can practice more efficiently, leading to faster improvements in their abilities. This could greatly enhance the development of robotic systems for various applications, making them more capable and reliable.
Abstract
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.