activity
20232026
collaborators

7 papers

cs.RO2026

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…

cs.RO2025

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…

cs.RO2025

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…

cs.LG2025

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…

cs.RO2024

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…

cs.RO2023

LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments

Chak Lam Shek, Xiyang Wu, Wesley A. Suttle +5

Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context fro…

cs.RO2023

Where to Drop Sensors from Aerial Robots to Monitor a Surface-Level Phenomenon?

Chak Lam Shek, Guangyao Shi, Ahmad Bilal Asghar +1

We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to drop unmovable sensors for monitoring a task area in the presence of stochastic w…