Training General Robots for Any Task: Physical Intelligence’s Karol Hausman and Tobi Springenberg

Training General Robots for Any Task: Physical Intelligence’s Karol Hausman and Tobi Springenberg

From Training Data by Sequoia Capital

January 6, 2026 · 1h 2m

About this episode

Karol Hausman and Tobi Springenberg discuss how foundation models can overcome intelligence bottlenecks in robotics.

Physical Intelligence’s Karol Hausman and Tobi Springenberg believe that robotics has been held back not by hardware limitations, but by an intelligence bottleneck that foundation models can solve. Their end-to-end learning approach combines vision, language, and action into models like π0 and π*0.6, enabling robots to learn generalizable behaviors rather than task-specific programs. The team prioritizes real-world deployment and uses RL from experience to push beyond what imitation learning alone can achieve. Their philosophy—that a single general-purpose model can handle diverse physical tasks across different robot embodiments—represents a fundamental shift in how we think about building intelligent machines for the physical world. Hosted by Alfred Lin and Sonya Huang, Sequoia Capital

People in this episode

Hosts: Alfred Lin, Sonya Huang

Guests: Karol Hausman, Tobi Springenberg

Topics covered

  • robotics
  • artificial intelligence
  • machine learning
  • end-to-end learning
  • physical intelligence

Keywords

  • robotics
  • foundation models
  • intelligence bottleneck
  • end-to-end learning
  • generalizable behaviors

Mentioned in this episode

Organizations: Physical Intelligence, Sequoia Capital

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