Mahdi Imani

Zeyu Fang, Yuxin Lin, Cheng Liu et al.

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.

Zeyu Fang, Beomyeol Yu, Cheng Liu et al.

Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively demanding for non-expert operators. To address this, we propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation. We introduce the Minimal Information Neuro-Symbolic Tree (MINT), a reasoning mechanism that explicitly structures knowledge gaps regarding obstacles and goals into a queryable format. By leveraging large language models, our system formulates optimal binary queries to resolve specific ambiguities with minimal human interaction. We demonstrate the efficacy of this approach through a comprehensive workflow integrating a vision-language model for perception, voice interfaces, and a low-level UAV control module in both high-fidelity NVIDIA Isaac simulations and real-world deployments. Experimental results show that our method achieves a significant improvement in the success rate for complex search-and-rescue tasks while significantly reducing the frequency of human interaction compared to exhaustive querying baselines.