We're sunsetting PodQuest on 2025-07-28. Thank you for your support!
Export Podcast Subscriptions
cover of episode Is robotics about to have its own ChatGPT moment?

Is robotics about to have its own ChatGPT moment?

2024/11/13
logo of podcast MIT Technology Review Narrated

MIT Technology Review Narrated
AI Deep Dive AI Chapters Transcript
People
C
Charlie Kemp
C
Chelsea Finn
D
Deepak Pathak
H
Henry Evans
J
Jane Evans
K
Ken Goldberg
L
Laura Pinter
R
Ross Allen
V
Vincent Vanhoucke
叙
叙述者
Topics
Jane Evans 指出家用机器人的主要挑战在于应对现实世界中家居环境的复杂性和不可预测性，例如家具摆放、地板规划以及宠物和儿童的活动等。这些因素使得机器人难以在非实验室环境中有效运行。 Charlie Kemp 认为该领域正处于转折点，廉价硬件、数据共享和生成式AI的进步使得机器人变得更胜任和更有帮助。 Ken Goldberg 阐述了莫拉维克悖论，即对人类来说容易的事情对机器来说很难，反之亦然。他指出机器人面临三大挑战：精确控制和协调能力不足；对周围世界的理解有限；缺乏对物理学的本能理解。 Deepak Pathak 强调了传统机器人训练方法的局限性，并指出人工智能的兴起正在改变机器人技术领域，重点从物理灵活性转向构建通用的机器人“大脑”。他介绍了强化学习和模仿学习这两种常用的AI训练机器人方法，并详细说明了其团队如何利用强化学习训练四足机器人进行复杂运动。 Ross Allen 认为模仿学习结合生成式AI，能够快速教会机器人许多新任务，并指出生成式AI有望在机器人领域引发类似于ChatGPT在语言模型领域所产生的影响。 Chelsea Finn 指出基于学习的方法在机器人领域越来越受欢迎，但需要大量特定于机器人的数据。目前数据稀缺，收集数据耗时费力。 Laura Pinter 进一步说明了数据稀缺的问题，并介绍了其团队开发的一种廉价的数据收集方法。她认为，要训练机器人完成更复杂的任务，需要更多的数据和演示。 Vincent Vanhoucke 认为大型视觉语言模型能够帮助机器人更好地理解周围世界，并进行推理和学习。谷歌DeepMind正在利用类似于机器翻译的技术，将自然语言指令转化为机器人的动作。 Henry Evans 分享了他使用机器人的经验，并强调了机器人带来的独立性，使他能够自己完成一些事情。

Deep Dive

Chapters
The dream of useful home robots has been elusive due to the unpredictability of home environments. However, a new generation of researchers believes that generative AI could revolutionize robotics by enabling robots to learn and adapt faster.
  • Home environments are unpredictable due to varying furniture, floor plans, and household activities.
  • Generative AI could give robots the ability to learn new skills and adapt to new environments quickly.
  • This new approach might finally bring robots out of the factory and into homes.

Shownotes Transcript

Robots that can do many of the things humans do in the home—folding laundry, cooking meals, cleaning—have been a dream of robotics research since the inception of the field in the 1950s. 

While engineers have made great progress in getting robots to work in tightly controlled environments like labs and factories, the home has proved difficult to design for. Out in the real, messy world, furniture and floor plans differ wildly; children and pets can jump in a robot’s way; and clothes that need folding come in different shapes, colors, and sizes. Managing such unpredictable settings and varied conditions has been beyond the capabilities of even the most advanced robot prototypes. 

But now, the field is at an inflection point. A new generation of researchers believes that generative AI could give robots the ability to learn new skills and adapt to new environments faster than ever before. This new approach, just maybe, can finally bring robots out of the factory and into the mainstream.

This story was written by senior AI reporter Melissa Heikkilä and narrated by Noa - newsoveraudio.com