1 citations · 1 across the 5 of their papers we have counts for
5 papers
Robust Multi-Agent Decision-Making in Finite-Population Games
Shinkyu Park, Lucas C. D. Bezerra
We study the robustness of an agent decision-making model in finite-population games, with a particular focus on the Kullback-Leibler Divergence Regularized Learning (KLD-RL) model…
Opinion-Driven Decision-Making for Multi-Robot Navigation through Narrow Corridors
Norah K. Alghamdi, Shinkyu Park
We propose an opinion-driven navigation framework for multi-robot traversal through a narrow corridor. Our approach leverages a multi-agent decision-making model known as the Nonli…
Learning Policies for Dynamic Coalition Formation in Multi-Robot Task Allocation
Lucas C. D. Bezerra, Ataíde M. G. dos Santos, Shinkyu Park
We propose a decentralized, learning-based framework for dynamic coalition formation in Multi-Robot Task Allocation (MRTA). Our approach extends MAPPO by integrating spatial action…
Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning
Chang Shu, Anton Kim, Shinkyu Park
This paper proposes an assembly sequence planning framework, named Subassembly to Assembly (S2A). The framework is designed to enable a robotic manipulator to assemble multiple par…
Threshold Decision-Making Dynamics Adaptive to Physical Constraints and Changing Environment
Giovanna Amorim, María Santos, Shinkyu Park +2
We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dy…