8 citations · 28 across the 23 of their papers we have counts for
24 papers
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…
Pre-Execution Safety Gate & Task Safety Contracts for LLM-Controlled Robot Systems
Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang +5
Large Language Models (LLMs) are increasingly used to convert task commands into robot-executable code, however this pipeline lacks validation gates to detect unsafe and defective…
Move-Then-Operate: Behavioral Phasing for Human-Like Robotic Manipulation
Haoming Xu, Lei Lei, Jie Gu +3
We present Move-Then-Operate, a Vision language action framework that explicitly decouples robotic manipulation into two distinct behavioral phases: coarse relocation (move) and co…
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…
AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory
Zhiqian Lan, Yuxuan Jiang, Ruiqi Wang +12
Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive modalities to generate action tra…
SafePlan: Leveraging Formal Logic and Chain-of-Thought Reasoning for Enhanced Safety in LLM-based Robotic Task Planning
Ike Obi, Vishnunandan L. N. Venkatesh, Weizheng Wang +5
Robotics researchers increasingly leverage large language models (LLM) in robotics systems, using them as interfaces to receive task commands, generate task plans, form team coalit…
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…
Modeling and Evaluating Trust Dynamics in Multi-Human Multi-Robot Task Allocation
Ike Obi, Ruiqi Wang, Wonse Jo +1
Trust is essential in human-robot collaboration, particularly in multi-human, multi-robot (MH-MR) teams, where it plays a crucial role in maintaining team cohesion in complex opera…
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…
Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering
Yasasa Abeysirigoonawardena, Kevin Xie, Chuhan Chen +4
Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safe…
Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning
Weizheng Wang, Le Mao, Ruiqi Wang +1
In public spaces shared with humans, ensuring multi-robot systems navigate without collisions while respecting social norms is challenging, particularly with limited communication.…
Robot Patrol: Using Crowdsourcing and Robotic Systems to Provide Indoor Navigation Guidance to The Visually Impaired
Ike Obi, Ruiqi Wang, Prakash Shukla +1
Indoor navigation is a challenging activity for persons with disabilities, particularly, for those with low vision and visual impairment. Researchers have explored numerous solutio…
NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning
Weizheng Wang, Ruiqi Wang, Le Mao +1
Developing robotic technologies for use in human society requires ensuring the safety of robots' navigation behaviors while adhering to pedestrians' expectations and social norms.…
Cognitive Load-based Affective Workload Allocation for Multi-human Multi-robot Teams
Wonse Jo, Ruiqi Wang, Baijian Yang +3
The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multi-robot systems. Adequately designed systems within this…
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…
MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks
Wonse Jo, Ruiqi Wang, Su Sun +3
This paper presents MOCAS, a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in…
Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition
Ruiqi Wang, Wonse Jo, Dezhong Zhao +4
Human state recognition is a critical topic with pervasive and important applications in human-machine systems. Multi-modal fusion, the combination of metrics from multiple data so…
SMARTmBOT: A ROS2-based Low-cost and Open-source Mobile Robot Platform
Wonse Jo, Jaeeun Kim, Ruiqi Wang +3
This paper introduces SMARTmBOT, an open-source mobile robot platform based on Robot Operating System 2 (ROS2). The characteristics of the SMARTmBOT, including low-cost, modular-ty…
Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation
Ruiqi Wang, Weizheng Wang, Byung-Cheol Min
Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching…