MIT CSAIL researchers have developed a generative AI system, LucidSim, to train robots in virtual environments for real-world navigation. Using ChatGPT and physics simulators, robots learn to traverse complex terrains. This method outperforms traditional training, suggesting a new direction for robotic training.
A team of roboticists and engineers at MIT CSAIL, Institute for AI and Fundamental Interactions, has developed a generative AI approach to teaching robots how to traverse terrain and move around objects in the real world.
The group has published a paper describing their work and possible uses for it on the arXiv preprint server. They also presented their ideas at the recent Conference on Robot Learning (CORL 2024), held in Munich Nov. 6–9.
Getting robots to navigate in the real world at some point involves teaching them to learn on the fly, or by training them with videos of similar robots in a real-world environment. While such training has proven to be effective in limited environments, it tends to fail when a robot encounters something novel. In this new effort, the team at MIT developed virtual training that better translates to the real world.
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