This course aims to teach learners about distributed perception and learning between robots and the cloud. The learning outcomes include understanding the key challenges of cloud robotics, distributed inference, and learning, as well as the accuracy and hidden costs associated with robot and cloud deep neural networks. The course covers topics such as network congestion, cloud offloading decision-making, reinforcement learning, and the benefits of specialization in correcting errors. The teaching method involves discussing theoretical concepts, practical experiments, and exploring real-world applications. This course is intended for individuals interested in robotics, cloud computing, artificial intelligence, and machine learning.
Stanford Seminar - Distributed Perception and Learning Between Robots and the Cloud
Stanford University via YouTube
Overview
Syllabus
Introduction.
Robot sensory data + compute models are becoming increasingly complex.
How Can Network Connectivity Help Robots?.
Key Challenges of Cloud Robotics.
1. Distributed Inference: The Robot-Cloud Offloading Problem.
2. Distributed Learning: The Robot Sensory Sampling Problem.
Outline.
Accuracy of Robot and Cloud DNNS.
Hidden Costs of Network Congestion.
Network Costs of Cloud Communication.
Our Network Congestion Experiments.
Cloud Offloading: A Dynamic Decision-Making Problem.
Robot-Cloud Offloading: Sequential Model Selection.
Reinforcement Learning (RL).
The Robot Offloading MDP: Action Space.
The Robot Offloading MDP: State Space.
The Robot Offloading MDP: Reward.
Deep RL beats benchmark offloading policies.
Can we make actionable insights from growing robotic sensory data?.
Rationale 1: Specialization corrects errors.
Model specialization can correct key errors.
Rationale 2: The real world is constantly changing.
Why sample?: Reduce systems costs.
Minimal Images are Needed.
Efficiently filter images of interest during inference.
Delegate compute-intensive tasks to the cloud.
Current: Multi-Robot Learning.
Task-Driven Representations for Perception.
Semi) Federated Learning for Robots.
Control and Learning Across Data Boundaries.
Taught by
Stanford Online
