Stanford Seminar - Distributed Perception and Learning Between Robots and the Cloud
Offered By: Stanford University via YouTube
Course Description
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
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