Stanford Seminar - Distributed Perception and Learning Between Robots and the Cloud | Summary and Q&A

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January 17, 2020
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Stanford Online
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Stanford Seminar - Distributed Perception and Learning Between Robots and the Cloud

TL;DR

This content discusses how resource-constrained robots can use cloud computing services to improve their computer vision task performance, focusing on distributed perception and learning.

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Questions & Answers

Q: How can resource-constrained robots use cloud computing services to improve their computer vision tasks?

Resource-constrained robots can offload certain tasks to the cloud, such as object detection or semantic segmentation, to improve their computer vision task performance by leveraging more compute and power-hungry models.

Q: What are the challenges of distributed inference between robots and the cloud?

Challenges include network latency, slower inference time of larger deep neural networks in the cloud, and the need to efficiently manage system resources like network transfer, annotation time, and cloud storage.

Q: How can distributed learning help improve computer vision task performance in autonomous vehicles?

By sending interesting field data from autonomous vehicles to the cloud, models can be retrained and adapted to the real-time operating conditions of the vehicles, leading to improved computer vision task performance.

Q: What is the key focus of the research on cloud robotics?

The research focuses on limiting communication by selectively querying the cloud, leveraging the benefits of cloud computing and robotics while minimizing end-to-end systems costs.

Summary & Key Takeaways

  • Robots face challenges of handling growing volumes of sensory data, leading to the need for compute and power-hungry models like deep neural networks.

  • Distributed inference enables robots to offload certain tasks to the cloud, improving real-time inference results.

  • Distributed learning allows robots to send data to the cloud for model retraining, improving computer vision task performance.

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