Dr. Sergey Levine
UC Berkeley / Physical Intelligence
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Sergey Levine. Every entry undergoes editorial source verification.
End-to-End Training of Deep Visuomotor Policies
Published the foundational Journal of Machine Learning Research paper proving that robotic controllers can map raw camera pixels directly to continuous motor torque voltages using guided policy search.
Direct pixel-to-torque policies are vulnerable to novel environmental lighting and visual background shifts.
π₀ (pi_0): Generalist Physical Intelligence Robot Foundation Model
Co-developed π₀, a generalist physical intelligence foundation model trained across diverse robotic embodiments and tasks, demonstrating zero-shot physical manipulation, folding laundry, and assembly.
Physical real-world manipulation requires sub-millisecond control loops and collision avoidance safeguards.