Robotics, simulation and multimodal AI
At NVIDIA’s March 2024 GTC, robotics, simulation and multimodal models appeared increasingly connected. I am interested in what that combination could make easier to build, especially for teams trying to move from a software prototype to a working machine.
The business question is whether these tools can reduce the cost of developing and adapting robotic systems. That would give more teams a chance to work on specialised applications.
Other research groups are working on related problems. What interests me here is the combination of models, simulation tools and hardware.
A multimodal model can work with both text and images. For a robot, that could mean relating an instruction to what its cameras show. Turning that interpretation into a reliable physical action still requires control systems, training and testing.
Consider a robot inspecting toys. A team could give it a description of an acceptable product and images of both intact and defective examples.
A model trained on descriptions and examples might help identify defects without a separate rule for every visible variation. Its performance would still need to be tested against representative examples, including failures that are rare or difficult to see.
Testing before deployment
3D simulation lets a team try changes in a virtual environment before using physical equipment. It can reduce the cost of some experiments and expose problems earlier, provided the simulation represents the properties that matter to the task.
3D simulation goes hand-in-hand with digital twins, providing a sandbox for AI and robotics to train, learn, and evolve in a virtual environment. This is particularly beneficial for AI models like the multimodal LLMs mentioned earlier. Instead of solely learning from past data, these AI systems can interact with virtual models of the world, learning through trial and error in a controlled virtual environment. This accelerates the learning process, enabling more complex and nuanced understanding of tasks before applying this knowledge in the real world.
Project GR00T brings together several techniques for humanoid robot learning:
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Simulation: NVIDIA trains the model in simulated environments with the help of GPUs (Graphics Processing Units) for accelerated performance. This allows robots to experiment and learn from their mistakes in a safe virtual space.
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Imitation learning: GR00T observes and imitates human actions from video data. By analyzing these demonstrations, the model learns to perform tasks and acquire new skills.
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Reinforcement learning: NVIDIA's Isaac Lab platform incorporates reinforcement learning, where the model receives rewards for achieving goals and penalties for mistakes. This method helps the robot learn through trial and error in a simulated environment.
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Multimodal learning: The model can take various inputs like natural language instructions and past interactions to determine the robot's course of action.
Learning from video
OpenAI’s February 2024 report on Sora raises a different possibility: learning aspects of a world model from video. It also describes important limitations, including errors in the physics of basic interactions. A convincing video is insufficient evidence that a model can reliably simulate a robot.
For robotics, I see this as a research direction. Generated video may eventually help with training data or planning, but those uses need validation against physical behaviour. Explicit simulation gives engineers control over parameters that are difficult to recover from video alone.
The opportunity I find most interesting is a smaller team being able to test a specific robotic task earlier. The useful measure will be how well that task works on hardware, including the cases that looked easy in a demonstration.
