DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation

TL;DR

DeCoNav introduces a decentralized, dialogue-driven framework for multi-robot vision-language navigation, enabling real-time adaptive coordination and significantly improving success rates in complex shared environments.

Contribution

It presents DeCoNav, a novel framework combining event-triggered dialogue with dynamic task reallocation for improved multi-robot collaboration.

Findings

DeCoNav improves success rate (BSR) by 69.2% in benchmark tests.

The framework enables real-time, adaptive coordination without a central controller.

DeCoNav is validated on 1,213 tasks across 176 HM3D scenes.

Abstract

Long-horizon collaborative vision-language navigation (VLN) is critical for multi-robot systems to accomplish complex tasks beyond the capability of a single agent. CoNavBench takes a first step by introducing the first collaborative long-horizon VLN benchmark with relay-style multi-robot tasks, a collaboration taxonomy, along with graph-grounded generation and evaluation to model handoffs and rendezvous in shared environments. However, existing benchmarks and evaluations often do not enforce strictly synchronized dual-robot rollout on a shared world timeline, and they typically rely on static coordination policies that cannot adapt when new cross-agent evidence emerges. We present Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation (DeCoNav), a decentralized framework that couples event-triggered dialogue with dynamic task allocation and replanning for real-time, adaptive coordination. In DeCoNav, robots exchange compact semantic states via dialogue without a central controller. When informative events such as new evidence, uncertainty, or conflicts arise, dialogue is triggered to dynamically reassign subgoals and replan under synchronized execution. Implemented in DeCoNavBench with 1,213 tasks across 176 HM3D scenes, DeCoNav improves the both-success rate (BSR) by 69.2%, demonstrating the effectiveness of dialogue-driven, dynamically reallocated planning for multi-robot collaboration.