Paper
Uncertainty Mitigation and Intent Inference: A Dual-Mode Human-Machine Joint Planning System
Authors
Zeyu Fang, Yuxin Lin, Cheng Liu, Beomyeol Yu, Zeyuan Yang, Rongqian Chen, Taeyoung Lee, Mahdi Imani, Tian Lan
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.07822v1</id>\n <title>Uncertainty Mitigation and Intent Inference: A Dual-Mode Human-Machine Joint Planning System</title>\n <updated>2026-03-08T21:43:30Z</updated>\n <link href='https://arxiv.org/abs/2603.07822v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.07822v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-03-08T21:43:30Z</published>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Zeyu Fang</name>\n </author>\n <author>\n <name>Yuxin Lin</name>\n </author>\n <author>\n <name>Cheng Liu</name>\n </author>\n <author>\n <name>Beomyeol Yu</name>\n </author>\n <author>\n <name>Zeyuan Yang</name>\n </author>\n <author>\n <name>Rongqian Chen</name>\n </author>\n <author>\n <name>Taeyoung Lee</name>\n </author>\n <author>\n <name>Mahdi Imani</name>\n </author>\n <author>\n <name>Tian Lan</name>\n </author>\n </entry>"
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