10 papers
Model-Free Adaptive Parameter Tuning for Efficient Multi-Robot Warehouse Operations
Pratap Tokekar, Mouhacine Benosman, Rahul Chandan +3
Robotic Fulfillment Centers (FCs) store inventory on shelves (pods) arranged in dense blocks. Retrieving a target pod that is buried deep in a block requires moving obstructing pod…
Offline Reinforcement Learning for Warehouse SLAM Throughput Control
Tina Dongxu Li, Mouhacine Benosman, Rajat Kumar +3
We present an offline reinforcement learning (RL) framework for optimizing SLAM throughput control in a warehouse fulfillment environment. SLAM (Scan/Label/Apply/Manifest) throughp…
A Comparative Study of Bayesian Contextual Bandits for Real-Time Warehouse Sorter Optimization
Tina Dongxu Li, Mouhacine Benosman, Ken Meszaros +1
Efficient sorter diversion control of automated material handling systems (MHS) is critical for optimizing operational efficiency in large-scale warehouse environments. In this stu…
Designing Psychometric Bias Measures for ChatBots: An Application to Racial Bias Measurement
Mouhacine Benosman
Artificial intelligence (AI), particularly in the form of large language models (LLMs) or chatbots, has become increasingly integrated into our daily lives. In the past five years,…
GRAM: Generalization in Deep RL with a Robust Adaptation Module
James Queeney, Xiaoyi Cai, Alexander Schperberg +3
The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scen…
AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
Jonah Botvinick-Greenhouse, Wael H. Ali, Mouhacine Benosman +1
We introduce adaptive-basis physics-informed neural networks (AB-PINNs), a novel approach to domain decomposition for training PINNs in which existing subdomains dynamically adapt…