"Perception is quite difficult with cameras: even if you have a stereo camera, you still can’t really build a map of where everything is in space. It’s just very difficult. And I know that sounds surprising, because humans are very good at this. In fact, even with one eye, we can navigate and we can clear the dinner table. But it seems that we’re building in a lot of understanding and intuition about what’s happening in the world and where objects are and how they behave. For robots, it’s very difficult to get a perfectly accurate model of ...
"Perception is quite difficult with cameras: even if you have a stereo camera, you still can’t really build a map of where everything is in space. It’s just very difficult. And I know that sounds surprising, because humans are very good at this. In fact, even with one eye, we can navigate and we can clear the dinner table. But it seems that we’re building in a lot of understanding and intuition about what’s happening in the world and where objects are and how they behave. For robots, it’s very difficult to get a perfectly accurate model of the world and where things are. So if you’re going to go manipulate or grasp an object, a small error in that position will maybe have your robot crash into the object, a delicate wine glass, and probably break it. So the perception and the control are both problems." —Ken Goldberg
In today’s episode, host Luisa Rodriguez speaks to Ken Goldberg — robotics professor at UC Berkeley — about the major research challenges still ahead before robots become broadly integrated into our homes and societies.
Links to learn more, highlights, and full transcript.
They cover:
- Why training robots is harder than training large language models like ChatGPT.
- The biggest engineering challenges that still remain before robots can be widely useful in the real world.
- The sectors where Ken thinks robots will be most useful in the coming decades — like homecare, agriculture, and medicine.
- Whether we should be worried about robot labour affecting human employment.
- Recent breakthroughs in robotics, and what cutting-edge robots can do today.
- Ken’s work as an artist, where he explores the complex relationship between humans and technology.
- And plenty more.
Chapters:
- Cold open (00:00:00)
- Luisa's intro (00:01:19)
- General purpose robots and the “robotics bubble” (00:03:11)
- How training robots is different than training large language models (00:14:01)
- What can robots do today? (00:34:35)
- Challenges for progress: fault tolerance, multidimensionality, and perception (00:41:00)
- Recent breakthroughs in robotics (00:52:32)
- Barriers to making better robots: hardware, software, and physics (01:03:13)
- Future robots in home care, logistics, food production, and medicine (01:16:35)
- How might robot labour affect the job market? (01:44:27)
- Robotics and art (01:51:28)
- Luisa's outro (02:00:55)
Producer: Keiran Harris
Audio engineering: Dominic Armstrong, Ben Cordell, Milo McGuire, and Simon Monsour
Content editing: Luisa Rodriguez, Katy Moore, and Keiran Harris
Transcriptions: Katy Moore
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