Summary of Tango: Training-free Embodied Ai Agents For Open-world Tasks, by Filippo Ziliotto et al.
TANGO: Training-free Embodied AI Agents for Open-world Tasks
by Filippo Ziliotto, Tommaso Campari, Luciano Serafini, Lamberto Ballan
First submitted to arxiv on: 5 Dec 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 This paper introduces TANGO, a novel approach that extends the capabilities of Large Language Models (LLMs) to create embodied agents capable of observing and acting in the world. Building upon existing LLMs’ ability to compose modules for complex reasoning tasks on images, TANGO integrates this capability with a PointGoal Navigation model and memory-based exploration policy as primitives. By prompting an LLM to compose these primitives, it can solve diverse tasks without additional training, achieving state-of-the-art results in three Embodied AI tasks: Open-Set ObjectGoal Navigation, Multi-Modal Lifelong Navigation, and Open Embodied Question Answering, even in zero-shot scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a super-smart computer program that can learn to do lots of things just by looking at and interacting with the world. This paper proposes a new way to make this happen using something called Large Language Models (LLMs). It’s like taking all the things an LLM is good at, like solving puzzles on images, and applying them to robots or other machines that can move around and interact with objects. The program can even solve different tasks without needing to learn each one separately, just by being shown a few examples of how it should work. This approach achieves amazing results in three important areas: navigating through spaces, answering questions about the world, and learning from experience. |
Keywords
» Artificial intelligence » Multi modal » Prompting » Question answering » Zero shot
