Summary of Spatial Reasoning and Planning For Deep Embodied Agents, by Shu Ishida
Spatial Reasoning and Planning for Deep Embodied Agents
by Shu Ishida
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper explores the development of data-driven techniques for spatial reasoning and planning tasks, focusing on enhancing learning efficiency, interpretability, and transferability across novel scenarios. The authors propose four key contributions: CALVIN, a differential planner that learns interpretable models of the world; SOAP, an RL algorithm that discovers options unsupervised for long-horizon tasks; LangProp, a code optimisation framework using LLMs to solve embodied agent problems; and Voggite, an embodied agent with a vision-to-action transformer backend. These contributions are evaluated on various benchmarks, including Atari games, Minecraft, and CARLA autonomous driving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how computers can plan and make decisions like humans do. The authors developed new ways for computers to learn from data and improve their performance over time. They created four tools that can help robots or other computers make better decisions: one tool learns to plan ahead, another finds the best way to solve a problem, a third creates code that is easy to understand, and the fourth uses computer vision to perform complex tasks. These tools were tested on different games and challenges and showed promising results. |
Keywords
» Artificial intelligence » Transferability » Transformer » Unsupervised
