DISSERTATION DEFENSE

Author : Md Abir Hossen
Advisors: Dr. Pooyan Jamshidi
Date: May 22, 2026
Time: 10:30 am
Place: Virtual (Zoom)
Link:  https://sc-edu.zoom.us/j/88165893836?pwd=wVzSgMF42SJFqBHkE7QtSQO1Tsemp4…

Abstract

Cyber-physical robotic systems expose a combinatorially large configuration space comprising interacting hardware and software parameters. Incorrect configurations can lead to functional faults that are difficult to diagnose due to the intricate and often hidden dependencies between system settings and performance. This dissertation addresses these challenges by learning and exploiting causal structure to enable robust, sample-efficient, and transferable configuration optimization.

The first part of this work introduces CaRE (Causal Robotics DEbugging), a causal diagnosis framework that identifies the root causes of observed functional faults. By learning causal relationships between configuration parameters and performance indicators from observational data, CaRE enables precise fault localization and validation through targeted interventions across both simulation and physical robot platforms.

Building on this causal foundation, the next stage introduces CURE (Causal Understanding and Remediation for Enhancing Robot Performance), a configuration optimization method that identifies causally relevant parameters and restricts optimization to a reduced subspace. CURE improves convergence efficiency and supports transfer across environments by leveraging causal knowledge obtained in low-cost simulations and applying it to real-world robot deployments.

The final part of this dissertation introduces RESCUE (REducing  Sampling cost with Causal Understanding and Estimation), which extends the optimization setting from a single-fidelity target environment to multi-fidelity settings where multiple information sources with different costs and accuracies are available. RESCUE uses causal structure to construct a causal prior and guide configuration-fidelity selection, reducing costly high-fidelity evaluations while preserving optimization quality. Empirical evaluation across synthetic and real-world problems shows improved sample efficiency, more effective fidelity allocation, and lower constraint violation rates than competing methods.

Collectively, this dissertation establishes a causal foundation for reliable, efficient, and transferable configuration debugging and optimization in cyber-physical systems, validated through both synthetic benchmarks and real-world robotic applications.