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Mohammad Abdullah Al Faruque

4 accepted papers

2026

Collaborative Task Allocation for Heterogeneous Multi-Robot Systems Through Iterative Clustering

David R. Martin, Brooks A. Butler, Scott Nivison, Magnus Egerstedt, Mohammad Abdullah Al Faruque, Pramod P. Khargonekar
RA-L 2026

Multi-robot systems face the challenge of efficiently allocating teams of heterogeneous robots to tasks. The task allocation problem is complicated by collaborative interactions between robots where teams of robots develop emergent capabilities that enable them to complete tasks that would be ineffi

Cited by 1Source ↗Scholar ↗
2026

Collaborative Task Allocation for Heterogeneous Multi-Robot Systems through Iterative Clustering

David R. Martin, Brooks A. Butler, Scott Nivison, Magnus Egerstedt, Mohammad Abdullah Al Faruque, Pramod Khargonekar
ICRA 2026poster

Multi-robot systems face the challenge of efficiently allocating teams of heterogeneous robots to tasks. The task allocation problem is complicated by collaborative interactions between robots where teams of robots develop emergent capabilities that enable them to complete tasks that would be ineffi…

Cited by 0Source ↗Scholar ↗
2025

Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception

Luke Chen, Junyao Wang, Trier Mortlock, Pramod Khargonekar, Mohammad Abdullah Al Faruque
CVPR 2025poster

Uncertainty Quantification (UQ) is crucial for ensuring the reliability of machine learning models deployed in real-world autonomous systems. However, existing approaches typically quantify task-level output prediction uncertainty without considering epistemic uncertainty at the multimodal feature f…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition

Junyao Wang, Mohammad Abdullah Al Faruque
ICASSP 2025accepted

Deep learning has been widely adopted for human activity recognition (HAR) while generalizing a trained model across diverse users and scenarios remains challenging due to distribution shifts (DS). The inherent low-resource challenge in HAR, i.e., collecting and labeling adequate human-involved data…

Cited by 0Source ↗Scholar ↗