7 papers
KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning
Chak Lam Shek, Faizan M. Tariq, Sangjae Bae +2
Heterogeneous multi-robot systems are increasingly used in long-horizon missions requiring coordinated planning across diverse capabilities. However, existing planning approaches s…
Contextual Neural Moving Horizon Estimation for Robust Quadrotor Control in Varying Conditions
Kasra Torshizi, Chak Lam Shek, Khuzema Habib +3
Adaptive controllers on quadrotors typically rely on estimation of disturbances to ensure robust trajectory tracking. Estimating disturbances across diverse environmental contexts…
Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
Chak Lam Shek, Guangyao Shi, Pratap Tokekar
Multi-agent reinforcement learning (MARL) requires coordinated and stable policy updates among interacting agents. Heterogeneous-Agent Trust Region Policy Optimization (HATRPO) enf…
When to Localize? A Risk-Constrained Reinforcement Learning Approach
Chak Lam Shek, Kasra Torshizi, Troi Williams +1
In a standard navigation pipeline, a robot localizes at every time step to lower navigational errors. However, in some scenarios, a robot needs to selectively localize when it is e…
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
Chak Lam Shek, Amrit Singh Bedi, Anjon Basak +5
In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textb…
Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning
Chak Lam Shek, Pratap Tokekar
Large Language Models (LLMs) have shown remarkable promise in reasoning and decision-making, yet their integration with Reinforcement Learning (RL) for complex robotic tasks remain…