Summary of Collaborative Instance Object Navigation: Leveraging Uncertainty-awareness to Minimize Human-agent Dialogues, by Francesco Taioli et al.
Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues
by Francesco Taioli, Edoardo Zorzi, Gianni Franchi, Alberto Castellini, Alessandro Farinelli, Marco Cristani, Yiming Wang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes a new task setting, Collaborative Instance Object Navigation (CoIN), where an embodied agent actively resolves uncertainties about the target instance during navigation in natural, template-free, open-ended dialogues with humans. To achieve this, the authors introduce Agent-user Interaction with Uncertainty Awareness (AIUTA), a novel training-free method that operates independently from the navigation policy and focuses on human-agent interaction reasoning using Vision-Language Models (VLMs) and Large Language Models (LLMs). AIUTA consists of a Self-Questioner model that initiates a self-dialogue to obtain a complete and accurate observation description with uncertainty estimation, followed by an Interaction Trigger module that determines whether to ask questions, continue, or halt navigation. The authors also introduce CoIN-Bench, a curated dataset for challenging multi-instance scenarios, supporting both online evaluation with humans and reproducible experiments with simulated user-agent interactions. The results show that AIUTA serves as a competitive baseline, outperforming existing language-driven instance navigation methods in complex scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for an agent to navigate to an object by asking questions to a human during the process. This helps the agent to get more accurate information about the object and make better decisions. The authors propose a method that allows the agent to ask questions, get answers, and then use that information to decide what to do next. They also created a dataset with many examples of this kind of interaction and tested their method on it. The results show that their method is very good at navigating to objects in complex scenes. |
