EMBER x HIC Seminar
Fall 2026 Co-hosted with CITRIS and the Banatao Institute and Humanoid Intelligence Center (HIC) at BAIR
Most Wednesdays, 11 a.m. – noon Banatao Auditorium (Floor 3), Sutardja Dai Hall, UC Berkeley
No registration required. Hosted in person; recordings available afterwards.
Matt Mason
Carnegie Mellon University
Why Is Robotics Hard?
Abstract
Is robotics hard? Moravec's paradox suggests that it is. Moravec's paradox says that the things we expected to be hard, like playing champion-level chess, turned out to be easy. And the things we expected to be easy, like moving the chess pieces, turned out to be hard. This seems to be born out in recent experience — after 50 years of work our robots still cannot move chess pieces as well as any eight-year-old child. One explanation is that manipulation is fundamentally hard. But there is another explanation: roboticists are dense. This talk will explore both possibilities.
Coming up
- Nov 18
Karen Adolph · NYU
- Dec 2
Stephanie Gil · Harvard University
- Dec 4
Luca Carlone · MIT
- Dec 14
Maryam Kamgarpour · EPFL
Past Talks (25)
Lining Yao (UC Berkeley): Fluidic Shape Morphing Skin for Robotics
Guanya Shi (Carnegie Mellon University): Toward Generalist Humanoids: Learning from Human and Robot Experience
Aaron Ames (Caltech): Safe Autonomy for Humanoid Robots
Joseph Moore (Johns Hopkins University): Defying the Limits: Safe Control for Highly Agile Robots in Extreme Environments
Dimitra Panagou (University of Michigan Ann Arbor)
Shuran Song (Stanford University): Making Manipulation as Simple as Possible but Not Simpler
Cathy Wu (MIT): Tackling the Long Tail of Transportation Optimization with Machine Learning
Mac Schwager (Stanford): How General are Generalist Robot Policies?
Leslie Kaelbling (MIT): Rational Robots
Jaime Fernández Fisac (Princeton): Scaling Safety-Critical Control Without Surrendering Guarantees
Allison Okamura (Stanford): When Robots Care: Assistive Medical Robotics from Hospital to Home
David Hyunchul Shim (KAIST): From Drones to Cars to Generalist Approaches
Alison Gopnik (UC Berkeley): Causal Learning as Empowerment Gain
Carlo Sferrazza (Amazon FAR): Humanoid Robot Learning
Sergey Levine (UC Berkeley): Robot Foundation Models
Rodney Brooks (MIT): Designing Embodied Intelligences with Which People Will Willingly Cohabit
Zen Luo (NVIDIA): Building Humanoid Behavior Foundation Models
Madhur Behl (UVA): Bringing AI Up to Speed
Pannag Sanketi (Google DeepMind): Achieving Human Level Competitive Robot Table Tennis - The Journey
Maani Ghaffari (UMich): Computational Symmetry and Learning for Robotics
John Bicket (Samsara): Making a Real World Impact
Haozhi Qi (UC Berkeley): The Atomic Skill Approach for Robot Dexterity
Giuseppe Loianno (UC Berkeley): Learning Robot Super Autonomy
Soon-Jo Chung (Caltech): Learning and Decision Making for Agile Robots Under Failure