4 citations · 8 across the 6 of their papers we have counts for
6 papers
Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems
Taehyeon Kim, Vishnunandan L. N. Venkatesh, Byung-Cheol Min
Learning coordinated behaviors for multi-robot systems from only a few demonstrations is difficult because temporal task dependencies and spatial trajectory generation are tightly…
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…
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…
Semantic Layering in Room Segmentation via LLMs
Taehyeon Kim, Byung-Cheol Min
In this paper, we introduce Semantic Layering in Room Segmentation via LLMs (SeLRoS), an advanced method for semantic room segmentation by integrating Large Language Models (LLMs)…
DynaCon: Dynamic Robot Planner with Contextual Awareness via LLMs
Gyeongmin Kim, Taehyeon Kim, Shyam Sundar Kannan +3
Mobile robots often rely on pre-existing maps for effective path planning and navigation. However, when these maps are unavailable, particularly in unfamiliar environments, a diffe…