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Gradient Dissent: Conversations on AI
Shaping the World of Robotics with Chelsea Finn
Feb 15, 2024
In this episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning. Topics include two-armed robots learning to cook shrimp, the challenges of developing humanoid and quadruped robots, the limitations of simulated environments, and the future of household robotics. She also talks about her work on student feedback in education and the impact of AI coding tools on computer science education.
53:46
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Quick takeaways
- Robots struggle with generalization and adapting to new situations, unlike humans who can readily adapt their skills to different scenarios.
- Training robots to perform complex tasks requires large amounts of data, and recent efforts have focused on aggregating data sets and fine-tuning pre-trained models to transfer knowledge to robot control.
Deep dives
The Challenges of Generalization in Robotics
Robots can perform impressive tasks in controlled environments, but they struggle with generalization. While videos of robots doing backflips or complex actions may suggest that they are ready for real-world applications, these systems are often tuned for specific scenarios. Changing the environment or interacting with new objects can cause their behavior to fail. Unlike humans, who can generally adapt their skills to different situations, robots struggle with such generalization. Research in robotics aims to not only improve robot dexterity but also enhance their ability to perform tasks in a wide range of scenarios.
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