13. Robotic System
Quick search
code
Show Source
GitHub 中文版
Machine Learning Systems: Design and Implementation
Table Of Contents
  • 1. Preface
  • 2. Introduction
    • 2.1. Machine Learning Applications
    • 2.2. Design Objectives of Machine Learning Frameworks
    • 2.3. Machine Learning Framework Architecture
    • 2.4. Application Scenarios of Machine Learning Systems
    • 2.5. Book Organization and Intended Audience
  • 3. Part I Framework Design
  • 4. Programming Model
    • 4.1. Overview
    • 4.2. Machine Learning Workflow
    • 4.3. Neural Network Programming
    • 4.4. Functional Programming
    • 4.5. Bridging Python and C/C++ Functions
    • 4.6. Chapter Summary
  • 5. AI Compiler Frontend
    • 5.1. Overview of AI Compilers
    • 5.2. Overview of AI Compiler Frontends
    • 5.3. Intermediate Representation
    • 5.4. Automatic Differentiation
    • 5.5. Type Systems and Static Analysis
    • 5.6. Frontend Compilation Optimization
    • 5.7. Chapter Summary
    • 5.8. Further Reading
  • 6. AI Compiler Backend
    • 6.1. Overview
    • 6.2. Graph Optimization
    • 6.3. Operator Selection
    • 6.4. Memory Allocation
    • 6.5. Operator Compiler
    • 6.6. Chapter Summary
    • 6.7. Further Reading
  • 7. Hardware Accelerator
    • 7.1. Overview
    • 7.2. Components of Hardware Accelerators
    • 7.3. Programming Methods
    • 7.4. Performance Optimization Methods
    • 7.5. Chapter Summary
  • 8. Distributed Training
    • 8.1. Overview
    • 8.2. Parallelism Methods
    • 8.3. Pipeline Parallelism with Micro-Batching
    • 8.4. Architecture of Machine Learning Clusters
    • 8.5. Collective Communication
    • 8.6. Parameter Server
    • 8.7. Federated Learning
    • 8.8. Training Large Language Models
    • 8.9. Chapter Summary
    • 8.10. Further Reading
  • 9. Model Deployment
    • 9.1. Overview
    • 9.2. Conversion to Inference Model and Model Optimization
    • 9.3. Model Compression
    • 9.4. Advanced Efficient Techniques
    • 9.5. Model Inference
    • 9.6. Security Protection of Models
    • 9.7. Chapter Summary
    • 9.8. Further Reading
  • 10. Part II Application Scenarios
  • 11. Recommender System
    • 11.1. Overview
    • 11.2. System Components
    • 11.3. Recommendation Pipeline
    • 11.4. Model Update
    • 11.5. Supporting Real-time Machine Learning
    • 11.6. Chapter Summary
    • 11.7. Further Reading
  • 12. Reinforcement Learning System
    • 12.1. Overview
    • 12.2. Introduction to Reinforcement Learning
    • 12.3. Single-Node Reinforcement Learning System
    • 12.4. Distributed Reinforcement Learning System
    • 12.5. Multi-agent Reinforcement Learning
    • 12.6. Multi-agent Reinforcement Learning System
    • 12.7. Chapter Summary
  • 13. Robotic System
    • 13.1. Overview of Robotic Systems
    • 13.2. Robot Operating System
    • 13.3. Case Study: Using ROS
    • 13.4. Modern Robot Learning
    • 13.5. Chapter Summary
Machine Learning Systems: Design and Implementation
Table Of Contents
  • 1. Preface
  • 2. Introduction
    • 2.1. Machine Learning Applications
    • 2.2. Design Objectives of Machine Learning Frameworks
    • 2.3. Machine Learning Framework Architecture
    • 2.4. Application Scenarios of Machine Learning Systems
    • 2.5. Book Organization and Intended Audience
  • 3. Part I Framework Design
  • 4. Programming Model
    • 4.1. Overview
    • 4.2. Machine Learning Workflow
    • 4.3. Neural Network Programming
    • 4.4. Functional Programming
    • 4.5. Bridging Python and C/C++ Functions
    • 4.6. Chapter Summary
  • 5. AI Compiler Frontend
    • 5.1. Overview of AI Compilers
    • 5.2. Overview of AI Compiler Frontends
    • 5.3. Intermediate Representation
    • 5.4. Automatic Differentiation
    • 5.5. Type Systems and Static Analysis
    • 5.6. Frontend Compilation Optimization
    • 5.7. Chapter Summary
    • 5.8. Further Reading
  • 6. AI Compiler Backend
    • 6.1. Overview
    • 6.2. Graph Optimization
    • 6.3. Operator Selection
    • 6.4. Memory Allocation
    • 6.5. Operator Compiler
    • 6.6. Chapter Summary
    • 6.7. Further Reading
  • 7. Hardware Accelerator
    • 7.1. Overview
    • 7.2. Components of Hardware Accelerators
    • 7.3. Programming Methods
    • 7.4. Performance Optimization Methods
    • 7.5. Chapter Summary
  • 8. Distributed Training
    • 8.1. Overview
    • 8.2. Parallelism Methods
    • 8.3. Pipeline Parallelism with Micro-Batching
    • 8.4. Architecture of Machine Learning Clusters
    • 8.5. Collective Communication
    • 8.6. Parameter Server
    • 8.7. Federated Learning
    • 8.8. Training Large Language Models
    • 8.9. Chapter Summary
    • 8.10. Further Reading
  • 9. Model Deployment
    • 9.1. Overview
    • 9.2. Conversion to Inference Model and Model Optimization
    • 9.3. Model Compression
    • 9.4. Advanced Efficient Techniques
    • 9.5. Model Inference
    • 9.6. Security Protection of Models
    • 9.7. Chapter Summary
    • 9.8. Further Reading
  • 10. Part II Application Scenarios
  • 11. Recommender System
    • 11.1. Overview
    • 11.2. System Components
    • 11.3. Recommendation Pipeline
    • 11.4. Model Update
    • 11.5. Supporting Real-time Machine Learning
    • 11.6. Chapter Summary
    • 11.7. Further Reading
  • 12. Reinforcement Learning System
    • 12.1. Overview
    • 12.2. Introduction to Reinforcement Learning
    • 12.3. Single-Node Reinforcement Learning System
    • 12.4. Distributed Reinforcement Learning System
    • 12.5. Multi-agent Reinforcement Learning
    • 12.6. Multi-agent Reinforcement Learning System
    • 12.7. Chapter Summary
  • 13. Robotic System
    • 13.1. Overview of Robotic Systems
    • 13.2. Robot Operating System
    • 13.3. Case Study: Using ROS
    • 13.4. Modern Robot Learning
    • 13.5. Chapter Summary

13. Robotic System¶

This chapter introduces robotics — a major direction of machine learning — and robotic systems. The key aspects explored in this chapter are as follows:

  • Basic knowledge of robotic systems

  • Robotic perception system, planning system, and control system

  • General Robot Operating System (ROS)

  • 13.1. Overview of Robotic Systems
    • 13.1.1. Perception System
    • 13.1.2. Planning System
    • 13.1.3. Control System
    • 13.1.4. Robot Safety
  • 13.2. Robot Operating System
    • 13.2.1. ROS 2 Nodes
    • 13.2.2. ROS 2 Topics
    • 13.2.3. ROS 2 Services
    • 13.2.4. ROS 2 Parameters
    • 13.2.5. ROS 2 Actions
  • 13.3. Case Study: Using ROS
    • 13.3.1. Node Creation
    • 13.3.2. Parameter Reading
    • 13.3.3. Server-Client Service Mode
    • 13.3.4. Client
    • 13.3.5. Action Mode
    • 13.3.6. Action Client
  • 13.4. Modern Robot Learning
    • 13.4.1. Overview
    • 13.4.2. Robot Interaction Environments
    • 13.4.3. Robot Skill Learning
    • 13.4.4. Deployment in real environments
  • 13.5. Chapter Summary
Previous
12.7. Chapter Summary
Next
13.1. Overview of Robotic Systems