Data Skeptic

Graphs and ML for Robotics

Nov 4, 2024
Join Abhishek Paudel, a PhD Student at George Mason University specializing in robotics and machine learning. He shares fascinating insights into how graph neural networks can classify rooms and enhance robotic navigation. Explore the evolution of machine learning in robotics, and the impact of deep learning on perception and motion control. Abhishek discusses the integration of natural language processing and innovative graph-based methods for decision-making, highlighting their role in improving spatial awareness and learning from mistakes.
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INSIGHT

Dataset for Room Classification

  • Abhishek Paudel's research on room classification uses floor plans as a dataset.
  • The dataset originates from Japanese houses and apartments, providing room coordinates and labels.
INSIGHT

Graphs for Room Representation

  • Graphs offer a natural way to represent room connections in floor plans, similar to topological maps in robotics.
  • Nodes represent rooms, and edges connect rooms based on proximity, not necessarily direct navigation.
ANECDOTE

Features for Room Classification

  • Room classification uses features like area, length, width, door count, and parent-child room relationships.
  • These features help distinguish room types for machine learning algorithms.
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