2 citations · 5 across the 8 of their papers we have counts for
10 papers
Communication-Aware Heterogeneous Graph Learning for Decentralized Multi-Human Multi-Robot Task Allocation
Ziqin Yuan, Ruiqi Wang, Baijian Yang +1
Multi-human multi-robot (MH-MR) teams combine robotic autonomy with human expertise, but effective task allocation requires coordinating scarce, dynamically available human support…
Situation Perception: A Necessary Primitive to Artificial Superintelligence
Ziqin Yuan, Jaymari Chua
Current large language models are extraordinary statistical engines. They compress vast amounts of text into useful patterns and can explain science, write code, imitate reasoning,…
PrefMoE: Robust Preference Modeling with Mixture-of-Experts Reward Learning
Ziqin Yuan, Ruiqi Wang, Dezhong Zhao +2
Preference-based reinforcement learning offers a scalable alternative to manual reward engineering by learning reward structures from comparative feedback. However, large-scale pre…
PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
Ruiqi Wang, Dezhong Zhao, Ziqin Yuan +5
Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is o…
Multimodal Audio-based Disease Prediction with Transformer-based Hierarchical Fusion Network
Jinjin Cai, Ruiqi Wang, Dezhong Zhao +8
Audio-based disease prediction is emerging as a promising supplement to traditional medical diagnosis methods, facilitating early, convenient, and non-invasive disease detection an…
REBEL: Rule-based and Experience-enhanced Learning with LLMs for Initial Task Allocation in Multi-Human Multi-Robot Teaming
Arjun Gupte, Ruiqi Wang, Vishnunandan L. N. Venkatesh +4
Multi-human multi-robot teams are increasingly recognized for their efficiency in executing large-scale, complex tasks by integrating heterogeneous yet potentially synergistic huma…
Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team Heterogeneity and Dynamic Information Uncertainty
Ziqin Yuan, Ruiqi Wang, Taehyeon Kim +3
Task allocation in multi-human multi-robot (MH-MR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the…
Personalization in Human-Robot Interaction through Preference-based Action Representation Learning
Ruiqi Wang, Dezhong Zhao, Dayoon Suh +3
Preference-based reinforcement learning (PbRL) has shown significant promise for personalization in human-robot interaction (HRI) by explicitly integrating human preferences into t…
PrefMMT: Modeling Human Preferences in Preference-based Reinforcement Learning with Multimodal Transformers
Dezhong Zhao, Ruiqi Wang, Dayoon Suh +4
Preference-based reinforcement learning (PbRL) shows promise in aligning robot behaviors with human preferences, but its success depends heavily on the accurate modeling of human p…
PrefCLM: Enhancing Preference-based Reinforcement Learning with Crowdsourced Large Language Models
Ruiqi Wang, Dezhong Zhao, Ziqin Yuan +2
Preference-based reinforcement learning (PbRL) is emerging as a promising approach to teaching robots through human comparative feedback, sidestepping the need for complex reward e…