Uncertainty Mitigation and Intent Inference: A Dual-Mode Human-Machine Joint Planning System

TL;DR

This paper introduces a dual-mode joint planning system for human-robot collaboration that actively models human intent and mitigates uncertainties through communication and probabilistic reasoning, improving efficiency and reducing interaction costs.

Contribution

It presents a unified system combining active uncertainty mitigation with intent inference, enabling more human-like, efficient collaboration in open environments.

Findings

Reduces interaction costs by 51.9% in uncertainty mitigation.

Decreases task execution time by 25.4% in intent-aware cooperation.

Validated in both simulation and real-world UAV deployments.

Abstract

Effective human-robot collaboration in open-world environments requires joint planning under uncertain conditions. However, existing approaches often treat humans as passive supervisors, preventing autonomous agents from becoming human-like teammates that can actively model teammate behaviors, reason about knowledge gaps, query, and elicit responses through communication to resolve uncertainties. To address these limitations, we propose a unified human-robot joint planning system designed to tackle dual sources of uncertainty: task-relevant knowledge gaps and latent human intent. Our system operates in two complementary modes. First, an uncertainty-mitigation joint planning module enables two-way conversations to resolve semantic ambiguity and object uncertainty. It utilizes an LLM-assisted active elicitation mechanism and a hypothesis-augmented A^* search, subsequently computing an optimal querying policy via dynamic programming to minimize interaction and verification costs. Second, a real-time intent-aware collaboration module maintains a probabilistic belief over the human's latent task intent via spatial and directional cues, enabling dynamic, coordination-aware task selection for agents without explicit communication. We validate the proposed system in both Gazebo simulations and real-world UAV deployments integrated with a Vision-Language Model (VLM)-based 3D semantic perception pipeline. Experimental results demonstrate that the system significantly cuts the interaction cost by 51.9% in uncertainty-mitigation planning and reduces the task execution time by 25.4% in intent-aware cooperation compared to the baselines.