Summary of Learning 3d Robotics Perception Using Inductive Priors, by Muhammad Zubair Irshad
Learning 3D Robotics Perception using Inductive Priors
by Muhammad Zubair Irshad
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed thesis explores the concept of learning with structured inductive bias and priors to design approaches and algorithms that unlock the potential of principle-centric intelligence. This involves leveraging prior knowledge, often available through past experience and assumptions about how the world works, to help autonomous agents generalize better and adapt their behavior based on past experience. The research demonstrates the use of prior knowledge in three robotics perception problems: object-centric 3D reconstruction, vision and language for decision-making, and 3D scene understanding. To solve these challenging problems, various sources of prior knowledge are proposed, including geometry and appearance priors from synthetic data, modularity and semantic map priors, and semantic, structural, and contextual priors. The study explores the use of these priors in deep learning models to efficiently encode them and proposes ways to warm-start networks for transfer learning or use hard constraints to restrict action spaces. This research aims to build intelligent agents that require very little real-world data or data acquired only from simulation to generalize to highly dynamic and cluttered environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we can make AI models smarter by giving them more information before they start working on a problem. This is called “prior knowledge” and it’s like having some hints to help the model figure things out faster. The researchers tested this idea in three different areas: recognizing objects, making decisions based on pictures and words, and understanding 3D scenes. They used different types of prior knowledge, like information from simulations or assumptions about how the world works. This could be useful for building AI agents that can work well in new situations without needing a lot of training data. |
Keywords
» Artificial intelligence » Deep learning » Scene understanding » Synthetic data » Transfer learning
