Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues (2412.01250v3)

Abstract: Language-driven instance object navigation assumes that human users initiate the task by providing a detailed description of the target instance to the embodied agent. While this description is crucial for distinguishing the target from visually similar instances in a scene, providing it prior to navigation can be demanding for human. To bridge this gap, we introduce Collaborative Instance object Navigation (CoIN), a new task setting where the agent actively resolve uncertainties about the target instance during navigation in natural, template-free, open-ended dialogues with human. We propose a novel training-free method, Agent-user Interaction with UncerTainty Awareness (AIUTA), which operates independently from the navigation policy, and focuses on the human-agent interaction reasoning with Vision-LLMs (VLMs) and LLMs. First, upon object detection, a Self-Questioner model initiates a self-dialogue within the agent to obtain a complete and accurate observation description with a novel uncertainty estimation technique. Then, an Interaction Trigger module determines whether to ask a question to the human, continue or halt navigation, minimizing user input. For evaluation, we introduce CoIN-Bench, with a curated dataset designed for challenging multi-instance scenarios. CoIN-Bench supports both online evaluation with humans and reproducible experiments with simulated user-agent interactions. On CoIN-Bench, we show that AIUTA serves as a competitive baseline, while existing language-driven instance navigation methods struggle in complex multi-instance scenes. Code and benchmark will be available upon acceptance at https://intelligolabs.github.io/CoIN/

• The paper introduces AIUTA, a novel method combining self-questioning and entropy-based uncertainty estimation to refine object detection with minimal user input.
• It integrates a Self-Questioner module using VLMs and LLMs to autonomously enhance scene descriptions before involving human clarification.
• The method outperforms state-of-the-art techniques on the CoIN-Bench dataset, demonstrating efficient human-agent collaboration in real-world navigation.

Overview of Collaborative Instance Navigation: Leveraging Agent Self-Dialogue to Minimize User Input

The paper "Collaborative Instance Navigation: Leveraging Agent Self-Dialogue to Minimize User Input" presents a novel approach for instance navigation tasks where a human-agent collaboration occurs in real-time to effectively locate a target object within an unfamiliar environment. Existing embodied instance navigation frameworks often unrealistically presume fully detailed inputs from users at the onset, a condition that can be impractical in dynamic or real-world situations. This work introduces a task termed Collaborative Instance Navigation (CoIN) which allows an agent to actively engage with a human user to clarify uncertainties about the target object, thereby minimizing the necessity for exhaustive upfront user input.

Key Contributions

The proposed method, termed Agent-user Interaction with UncerTainty Awareness (AIUTA), incorporates elements such as a Self-Questioner and an Interaction Trigger. These components work in tandem to enable an agent to efficiently converse with itself using LLMs and Vision LLMs (VLMs) before escalating queries to a human user. Through this pipeline, the agent mitigates perceptual inaccuracies and hallucinations that typically complicate object recognition tasks.

For robust evaluation, the authors present CoIN-Bench, a novel benchmark dataset that simulates both real and virtual human interactions. This benchmark supports scalable experiments with a focus on real-world applicability and introduces performance metrics that reflect the efficiency of user-agent communications.

Technical Approach

1. Self-Questioner Module: At the heart of AIUTA, this module leverages a VLM to make an initial detection, after which an LLM is engaged to produce self-generated questions aimed at clarifying and enriching the initial detection description. Notably, a novel entropy-based technique is employed to quantify VLM uncertainty, filtering out equivocal attributes from the refined description.
2. Interaction Trigger Module: Using the refined scene description, this component decides on the necessity and type of user engagement, determining whether to continue exploration autonomously, solicit clarification from the user, or conclude the navigation endeavor. The decision-making process is driven by assessing the alignment score between observed object attributes and known facts about the target.

Results and Performance

AIUTA demonstrated a competitive edge over state-of-the-art techniques in CoIN-Bench assessments. The method's capability to continue navigation with minimal user interaction supports its agile handling of sparse and imprecise initial instructions. It exhibits a remarkable flexibility in interfacing with real human users, further evidenced by a structured ablation paper underpinning the crucial role of the Self-Questioner module in reducing perceptual errors, thereby bolstering detection accuracy.

Implications and Future Directions

This research situates itself at a pertinent intersection of robotics, language processing, and human-computer interaction, paving the way for more interactive and intelligent navigation systems that can seamlessly integrate human oversight with autonomous capability. The introduction of an entropy-based uncertainty estimation framework holds significant promise for enhancing LLM and VLM performance in dynamic contexts.

Future advancements could explore optimizing computational models to balance efficiency with large-scale perceptual tasks, reducing dependency on extensive cloud processing. Additionally, developing more lightweight architectures could increase real-world applicability particularly in privacy-sensitive domains. The benchmarks and methodologies introduced herein provide a critical foundation for subsequent explorations into interactive AI systems capable of more nuanced human-agent collaboration.