Blog
Field notes on building robot skills, from your first teleoperated demonstration to a deployed policy.
What is Physical AI?
AI that perceives and acts in the real world, and how imitation learning, reinforcement learning, and robotics fit together.
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The best teleoperation system for robot data collection
What makes a teleoperation data collection system good, how the main rigs compare, and how to make the data trainable.
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RGB vs. depth cameras for robot skill training
What RGB and depth cameras each capture, where each fails, and which your manipulation policy actually needs.
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What is a Vision-Language-Action (VLA) model?
How VLA models map camera frames and language instructions straight to robot actions, and where they fit.
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Imitation learning vs. reinforcement learning
When to clone demonstrations, when to reward-optimize, and why most policies use both.
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Robot skill scaling laws
How success rate scales with demonstrations, and what a new skill really costs in data and compute.
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How robot data collection actually works
Teleoperation rigs, what a demonstration actually records, and how to keep the data trainable.
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Sim-to-real: mixing simulated and real data
How much simulation helps, where it breaks, and how to blend sim and real rollouts.
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Atomic policies vs. end-to-end VLA models
Short, composable skills or one big model? The tradeoffs in reliability, data, and debugging.
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