Everything that moves will eventually become autonomous in AI robotics. Robots come in many forms—every car today is already a robot in real-world robotics. Now, we’re advancing toward building general-purpose robots in AI robotics and real-world robotics. At their core, physical AI and robotics share the same foundational challenges: where do you get the data for robotics foundation models, what is the model architecture for robotics foundation models, and how do we scale for autonomous robots in AI robotics?
We focus on the three essential pillars of the robotics industry in AI robotics:
These three function as a data flywheel: large-scale data builds better foundation models in robotics foundation models, which boost teleoperation efficiency; teleoperation then fuels more real-world data collection, completing the loop for autonomous robots in AI robotics.
Data at scale drives progress in AI robotics, but real-world robotics is a data deficient space for robotics foundation models.
Teleoperation improves performance and generates high quality data in AI robotics, but right now, every robotics company redoes the arduous work required to build a robust teleoperation stack [link to launch post: https://blog.prismax.ai/prismax-launches-ai-teleoperations-platform] in AI robotics.
Models bring everything together in AI robotics and robotics foundation models.