Summary of Human-object Interaction From Human-level Instructions, by Zhen Wu et al.
Human-Object Interaction from Human-Level Instructions
by Zhen Wu, Jiaman Li, Pei Xu, C. Karen Liu
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 system is a complete framework for generating physically plausible human-object interactions based on human-level instructions. It utilizes large language models to interpret input instructions and create detailed execution plans, including finger-object interactions that are coordinated with full-body movements. The system also employs reinforcement learning to track generated motions in physics simulation and ensure physical plausibility. The results demonstrate the effectiveness of the system in synthesizing realistic interactions with diverse objects in complex environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for robots or machines to understand and follow human instructions. It’s like teaching a robot how to do everyday tasks, but instead of just telling it what to do, you’re giving it detailed plans about how to do things. The system uses big language models to understand what the instruction means, and then creates a plan for how to carry out that task. This includes moving fingers in specific ways to manipulate objects. The system also makes sure its actions are physically possible by using simulation and reinforcement learning. The results show that this system can create realistic interactions with different objects in complex environments. |
Keywords
» Artificial intelligence » Reinforcement learning
