RoboBoom
CMU Robotics Institute· April 27, 2024· 1:11:48

RI Seminar: Dieter Fox: Where's RobotGPT?

Dieter Fox, professor at the University of Washington and robotics research lead at NVIDIA, discusses where robotics stands relative to generative AI and how simulation could help train more capable robots, during a CMU Robotics Institute seminar.

In this talk

  1. 1:30

    Why robotics needs more training data

    Fox says language and vision models benefit from vast datasets and scalable architectures, while robot manipulation lacks comparable action-labeled data. He proposes combining large-scale demonstrations with generative models and behavior cloning to improve manipulation, while stressing that this would not solve robotics on its own.

  2. 14:26

    What simulation can teach robots

    Fox describes using simulation to create labeled grasp data, train object segmentation, and develop manipulation policies that transfer to real robots. Examples include generating grasps for nearly 9,000 ShapeNet objects and training pose estimation on tens of thousands of simulated objects. He also covers contact-rich industrial tasks, where GPU simulation enabled 1,000 parallel runs in real time.

  3. 42:25

    Building a simulation training pipeline

    Fox outlines a pipeline for generating varied assets and scenes, creating tasks, solving them with planning or reinforcement learning, and rendering demonstrations for behavior cloning. He discusses procedural kitchens, AI-generated assets, language models for suggesting tasks, and task-and-motion planning for producing demonstrations at scale.

  4. 56:05

    Connecting robot policies with language models

    Fox contrasts systems where a vision-language model calls a fixed set of robot skills with end-to-end models that map images and language to robot controls. He suggests an intermediate approach that aligns language models with learned robot policies. He concludes that simulation data should be combined with real demonstrations and that real-world fine-tuning and reinforcement learning will still be needed.

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