Imagine having a robot that could help you clean up after a party — or fold heaps of laundry. Chengshu Eric Li and Josiah David Wong, two Stanford University Ph.D. students advised by renowned American computer scientist Professor Fei-Fei Li, are making that a dream come true. In this episode of the AI Podcast, host Noah Kravitz spoke with the two about their project, BEHAVIOR-1K, which aims to enable robots to perform 1,000 household chores, including picking up fallen objects or cooking. To train the robots, they’re using the NVIDIA Omniverse platform, as well as reinforcement and imitation learning techniques. Listen to hear more about the breakthroughs and challenges Li and Wong experienced along the way.
Stay tuned for more AI Podcast episodes recorded live from GTC.
Time Stamps
3:33: Background on the BEHAVIOR-1K project
5:00: Why use a simulated environment to train robots?
6:48: Why build a new simulation engine instead of using an existing one?
10:48: The process of training the robots to perform household chores
14:04: Some of the most complex tasks taught to the robots
19:07: How are large language models and large vision models affecting the progress of robotics?
24:09: What’s next for the project?
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