Collaborative Instance Navigation: Leveraging Agent Self-Dialogue to
  Minimize User Input
Authors: name: Francesco Taioli, name: Edoardo Zorzi, name: Gianni Franchi, name: Alberto Castellini, name: Alessandro Farinelli, name: Marco Cristani, name: Yiming Wang

Existing embodied instance goal navigation tasks, driven by natural language,
assume human users to provide complete and nuanced instance descriptions prior
to the navigation, which can be impractical in the real world as human
instructions might be brief and ambiguous. To bridge this gap, we propose a new
task, Collaborative Instance Navigation (CoIN), with dynamic agent-human
interaction during navigation to actively resolve uncertainties about the
target instance in natural, template-free, open-ended dialogues. To address
CoIN, we propose a novel method, Agent-user Interaction with UncerTainty
Awareness (AIUTA), leveraging the perception capability of Vision Language
Models (VLMs) and the capability of Large Language Models (LLMs). First, upon
object detection, a Self-Questioner model initiates a self-dialogue to obtain a
complete and accurate observation description, while a novel uncertainty
estimation technique mitigates inaccurate VLM perception. Then, an Interaction
Trigger module determines whether to ask a question to the user, continue or
halt navigation, minimizing user input. For evaluation, we introduce
CoIN-Bench, a benchmark supporting both real and simulated humans. AIUTA
achieves competitive performance in instance navigation against
state-of-the-art methods, demonstrating great flexibility in handling user
inputs.