5 citations · 13 across the 12 of their papers we have counts for
15 papers · 1 filter
Task Structure and Scalability in Multi-Robot Authoring
Christian Marowski, Byung-Cheol Min
Multi-robot authoring can reduce completion time by distributing drawing work, but the resulting speedup depends on task structure and the allocation-routing pipeline. We investiga…
MIND: Instruction-Conditioned Incremental Grounding for Robots
Mohamed Abouagour, Junhyun Nam, Wonse Jo +1
Robots following persistent language instructions must decide which cached room judgments remain valid as their environment changes. We present MIND (Maintenance via INstruction-co…
Audit Before You Commit: Locating Belief Failures in Active Identification for One-Shot Manipulation
Mohamed Abouagour, Byung-Cheol Min
A robot that probes a few times before one irreversible action, such as tapping a surface before inserting a peg, must decide when the evidence is enough to commit. We argue that t…
Containing Behavioral Cascades from Manipulated Claims in LLM-Powered Multi-Robot Systems
Waleed Bin Khalid, Byung-Cheol Min
Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-wide behavioral cascade, cau…
Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines
Syed T. Mubarrat, Byung-Cheol Min, Tianyu Shao +5
Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification…
Logic-Guided Socially-aware Robot Navigation World Model
Weizheng Wang, Obi Ike, Soyun Choi +3
Social robot navigation increasingly relies on large language models for reasoning, path planning, and enabling movement in dynamic human spaces. However, relying solely on LLMs fo…
REFLEX: Metacognitive Reasoning for Reflective Zero-Shot Robotic Planning with Large Language Models
Wenjie Lin, Jin Wei-Kocsis, Jiansong Zhang +4
While large language models (LLMs) have shown great potential across various domains, their applications in robotics remain largely limited to static prompt-based behaviors and sti…
Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation
Weizheng Wang, Ike Obi, Byung-Cheol Min
Recent advances in robotics and large language models (LLMs) have sparked growing interest in human-robot collaboration and embodied intelligence. To enable the broader deployment…
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…
EfficientEQA: An Efficient Approach to Open-Vocabulary Embodied Question Answering
Kai Cheng, Zhengyuan Li, Xingpeng Sun +3
Embodied Question Answering (EQA) is an essential yet challenging task for robot assistants. Large vision-language models (VLMs) have shown promise for EQA, but existing approaches…
Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation
Weizheng Wang, Chao Yu, Yu Wang +1
Navigating in human-filled public spaces is a critical challenge for deploying autonomous robots in real-world environments. This paper introduces NaviDIFF, a novel Hamiltonian-con…
Hypergraph-based Coordinated Task Allocation and Socially-aware Navigation for Multi-Robot Systems
Weizheng Wang, Aniket Bera, Byung-Cheol Min
A team of multiple robots seamlessly and safely working in human-filled public environments requires adaptive task allocation and socially-aware navigation that account for dynamic…
Unifying Large Language Model and Deep Reinforcement Learning for Human-in-Loop Interactive Socially-aware Navigation
Weizheng Wang, Ike Obi, Aniket Bera +1
Navigating human-filled spaces is crucial for the interactive social robots to support advanced services, such as cooperative carrying, which enables service provision in complex a…
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…
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.…