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Chapter 02: Vision Transformers

Overview​

This chapter explores how Vision Transformers (ViTs) are revolutionizing robot perception, enabling robots to understand visual scenes with unprecedented accuracy and generalization.

Learning Objectives​

  • Understand Vision Transformer architecture
  • Learn ViT applications in robotics
  • Explore attention mechanisms for vision
  • Understand multi-scale visual processing
  • Master ViT-based perception pipelines

Core Concepts​

1. Vision Transformer Architecture​

ViT Architecture:

Image → Patches → Embeddings → Transformer Encoder → Classification/Detection

Key Components:

  • Patch Embedding: Divide image into patches
  • Position Embedding: Add spatial information
  • Transformer Encoder: Self-attention layers
  • Classification Head: Task-specific output

2. Attention Mechanism​

Self-Attention Formula:

Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}})V

Where Q, K, V are query, key, and value matrices.

Summary​

Vision Transformers enable:

  • Better visual understanding
  • Improved generalization
  • Multi-scale feature learning
  • Attention to relevant regions