Show Notes
Humanoid robotics challenges go beyond movement and servo motors.
The hardest problems are often AI problems. Bringing intelligence into the physical world means dealing with gravity, friction, uncertainty, and real consequences. Mistakes can break hardware.
This week on Neuron Live, we’re joined by Nikita Rudin, Co-founder and CEO of Flexion Robotics, to unpack what it actually takes to build intelligence for humanoid systems.
What we’ll cover: 🤖 Training control policies and perception models 🧪 Bridging simulation and the real world (sim-to-real) 🛠️ Robotics training pipelines and the embodied AI stack 🔮 Where humanoid and physical AI is headed next
If large language models are the brain in the cloud, what does intelligence look like when it has to walk, grasp, and not fall over?
Expect a deep dive into embodied AI, physical AI, and the systems powering the next generation of humanoid robots.
More from The Neuron: AI Explained
Neurosurgeon-scientists Alex Ksendzovsky and Jon Pomeraniec explain how The Biological Computing Company uses living neurons as a research platform for AI. Their team grows roughly 150,000 cells on a 4,096-electrode array, sends in patterns such as handwritten digits, records how the neurons respond, and models useful computational behaviors in software. They say those neuron-inspired techniques are already helping GPU-based video-generation systems run about 5x faster with roughly 80% lower inference cost, while their longer-term work explores robots, world models, and real-time biological compute.
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Corey and Grant sit down with Mammoth Biosciences co-founder and CEO Dr. Trevor Martin to explain how CRISPR is moving from a laboratory tool toward one-time medicines.