Projects

Publications

2026

[C112] Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories
Yiyuan Pan, Xusheng Luo, Hanjiang Hu, Peiqi Yu and Changliu Liu
Robotics: Science and Systems, 2026
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[U] Contingency Planning for Safety-Critical Autonomous Vehicles: A Review and Perspectives
Lei Zheng, Luyao Zhang, Peiqi Yu, Yifan Sun, Sergio Grammatico, Jun Ma and Changliu Liu
arXiv:2601.14880, 2026
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[U] Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations
Peiqi Yu, Philip Huang, Chaitanya Chawla, Guanya Shi, Jiaoyang Li and Changliu Liu
arXiv:2603.12649, 2026
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2025

[W] NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing
Bowei Li, Peiqi Yu, Zhenran Tang, Han Zhou, Yifan Sun, Ruixuan Liu and Changliu Liu
RSS 2025 Workshop on Benchmarking Robot Manipulation: Improving Interoperability and Modularity, 2025
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Abstract:

This paper presents NeSyPack, a neuro-symbolic framework for bimanual logistics packing. Our NeSyPack combines data-driven models and symbolic reasoning to build an explainable hierarchical framework that is generalizable, data-efficient, and reliable. It decomposes a task into subtasks via hierarchical reasoning, and further into atomic skills managed by a symbolic skill graph. The graph selects skill parameters, robot configurations, and task-specific control strategies for execution. This modular design enables robustness, adaptability, and efficient reuse—outperforming end-to-end models that require large-scale retraining. Using NeSyPack, our team won the First Prize in the What Bimanuals Can Do (WBCD) competition at the 2025 IEEE International Conference on Robotics & Automation (ICRA).

2024

[U] Robustifying Long-term Human-Robot Collaboration through a Hierarchical and Multimodal Framework
Peiqi Yu, Abulikemu Abuduweili, Ruixuan Liu and Changliu Liu
arXiv:2411.15711, 2024
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Abstract:

Long-term Human-Robot Collaboration (HRC) is crucial for enabling flexible manufacturing systems and integrating companion robots into daily human environments over extended periods. This paper identifies several key challenges for such collaborations, such as accurate recognition of human plan, robustness to disturbances, operational efficiency, adaptability to diverse user behaviors, and sustained human satisfaction. To address these challenges, we model the long-term HRC task through a hierarchical task graph and presents a novel multimodal and hierarchical framework to enable robots to better assist humans to advance on the task graph. In particular, the proposed multimodal framework integrates visual observations with speech commands to facilitate intuitive and flexible human-robot interactions. Additionally, our hierarchical designs for both human pose detection and plan prediction allow better understanding of human behaviors and significantly enhance system accuracy, robustness, and flexibility. Moreover, an online adaptation mechanism enables real-time adjustment to diverse user behaviors. We deploy the proposed framework to KINOVA GEN3 robot and conduct extensive user studies on real-world long-term HRC assembly scenarios. Experimental results show that our approaches reduce task completion time by 15.9%, achieves an average task success rate of 91.8% and an overall user satisfaction score of 84% in long-term HRC tasks, showcasing its applicability in enhancing real-world long-term HRC.

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