Developed by researchers from Stephen James’s Robot Learning Lab in London, the system could make it easier to train different types of robots to complete tasks.
Researchers found that, by lifting actions into image-space, internet pre-trained diffusion models can generate policies that outperform state-of-the-art visuomotor approaches.
Called Genima, the new system fine-tunes Stable Diffusion to draw robots’ movements. It actually helps to guide robots in simulations and the real world. “We study GENIMA on 25 RLBench and 9 real-world manipulation tasks. We find that, by lifting actions into image-space, internet pre-trained diffusioncan generate policies that outperform state-of-the-art visuomotor approaches, especially in robustness to scene perturbations and generalizing to novel objects,”
Researchers claimed that Genima could be adapted to other embodiments, and also to draw physical attributes like forces and accelerations. Genima is quite capable, but not without limitations, according to the study.The study published here claims that like all BC-agents, Genima only distills expert behaviors and does not discover new behaviors. Genima also uses camera calibration to render targets, assuming the robot is always visible from some viewpoint.
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Humanoid robots, driverless cars can be trained using AI images with new methodDeveloped by researchers from Stephen James’s Robot Learning Lab in London, the system could make it easier to train different types of robots to complete tasks—machines ranging from mechanical arms to humanoid robots and driverless cars.
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