Ken Goldberg: Solving Robotics’ Hardest Problems: Dexterity, Data, and Design

Robotics in the home remains a long-term goal, currently constrained by a significant "robot data gap" compared to the massive datasets fueling large language models. While simulation excels at locomotion and navigation, manipulation—the physical interaction with objects—remains a complex hurdle due to the scarcity of high-dimensional training data. Ken Goldberg, a professor at UC Berkeley and co-founder of Ambi Robotics, argues that the path to generalist home robots requires a strategic progression from warehouse logistics and manufacturing to maintenance tasks. By leveraging real-world production data, such as the 100 million packages sorted by Ambi Robotics, the field can build more reliable models. This iterative "flywheel" of data collection and refinement, combined with advancements in vision-language models for tasks like block stacking, provides a viable roadmap toward robots capable of performing complex, unstructured tasks in human environments.