2 citations · 6 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
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…
Initial Task Assignment in Multi-Human Multi-Robot Teams: An Attention-enhanced Hierarchical Reinforcement Learning Approach
Ruiqi Wang, Dezhong Zhao, Arjun Gupte +1
Multi-human multi-robot teams (MH-MR) obtain tremendous potential in tackling intricate and massive missions by merging distinct strengths and expertise of individual members. The…
Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning
Ruiqi Wang, Dezhong Zhao, Byung-Cheol Min
Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise. To effi…