Summary of Wonderful Team: Zero-shot Physical Task Planning with Visual Llms, by Zidan Wang et al.
Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs
by Zidan Wang, Rui Shen, Bradly Stadie
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 Wonderful Team framework is a multi-agent Vision Large Language Model (VLLM) that enables zero-shot high-level robotic planning. Given an image of the robot’s surroundings and a task description, the VLLM outputs the sequence of actions necessary for the robot to complete the task. Unlike previous methods, Wonderful Team integrates perception, control, and planning using VLLMs, leading to improved performance on real-world tasks. For example, it achieves an average 40% success rate improvement over NLaP and Trajectory Generators on semantic and physical planning tasks, including drawing a plate and rearranging the environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Wonderful Team is a new way for robots to plan and complete tasks without being trained beforehand. It’s like having a super smart robot that can figure out what to do when it sees something new. This helps the robot make better decisions and get more things done. The results are impressive, with an average 40% improvement over other methods on tasks like drawing a plate or rearranging objects. |
Keywords
» Artificial intelligence » Large language model » Zero shot
