Summary of Llmphy: Complex Physical Reasoning Using Large Language Models and World Models, by Anoop Cherian et al.
LLMPhy: Complex Physical Reasoning Using Large Language Models and World Models
by Anoop Cherian, Radu Corcodel, Siddarth Jain, Diego Romeres
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Physical reasoning is crucial for robotic agents operating in real-world scenarios. Large Language Models (LLMs) struggle with hypothesizing and reflecting on complex multi-body interactions under various physical forces, posing a significant hurdle. To address this issue, we propose the TraySim dataset and task, which involves predicting the dynamics of objects on a tray after an external impact. Our zero-shot black-box optimization framework, LLMPhy, leverages the physics knowledge and program synthesis abilities of LLMs, synergizing with world models from modern physics engines. LLMPhy generates code to estimate physical hyperparameters via an implicit analysis-by-synthesis approach, using a simulator in the loop. We demonstrate the effectiveness of LLMPhy on TraySim, predicting steady-state poses of objects. Results show superior zero-shot physical reasoning performance compared to standard black-box optimization methods and better estimation of physical parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a tray with objects that can move around when something hits it. This is called the “domino effect”. Machines like robots need to understand how these objects will behave after the impact. Our team created a special dataset, TraySim, and designed an experiment to test this problem. We developed a new way to use Large Language Models (LLMs) that can solve this complex problem by analyzing the physical forces involved. This is called LLMPhy. It’s like having a super smart helper that can figure out how the objects will behave after the impact without needing any special training. Our results show that LLMPhy does an excellent job of predicting what will happen, beating other methods. |
Keywords
* Artificial intelligence * Optimization * Zero shot
