For robotics to work effectively, it needs to understand the physical environment around it, which is a significant challenge that has historically held back progress.

We created an 'arm farm' where robots learn to grasp objects by trial and error, improving their grasping abilities through pooled sensor data.

In robotics, we are particularly focused on manipulation, which is currently one of the most unresolved challenges in the field.

We need to address the challenges of the Physical Turing Test, as current robotics struggle to perform tasks that humans can do effortlessly.

Data collection for robotics is challenging; we can't scrape the necessary data from the internet, and we rely on expensive teleoperation methods to teach robots.

One of the early projects involved teaching a robot hand to solve a Rubik's Cube, aiming to demonstrate that AI could generalize beyond narrow tasks.

Current AI systems lack the ability to acquire common sense and learn from natural sensors like video, which presents a significant challenge.

Even if we wanted to implement Asimov's laws in robots, it would be immensely difficult because we currently lack the understanding to create AI that can comprehend these laws.

The 'Lego brick' approach to AI development involves quickly combining specialized tools rather than building everything from scratch.