PhD Thesis Proposal
Efficient Multi-Agent Motion Planning using Local Policies
Abstract: Teams of multiple robots working together can achieve challenging tasks like warehouse automation, search and rescue, and cooperative construction. However, finding efficient collision-free motions for all agents is extremely challenging as the complexity of the multi-agent motion planning (MAMP) problem grows exponentially with the number of agents. Multi-Agent Path Finding (MAPF) is a subset [...]
Scaling, Automating and Adapting Sim-to-real Policy Learning
Abstract: Building a generalist robot capable of performing diverse tasks in unstructured environments remains a longstanding challenge. A recent trend in robot learning aims to address this by scaling up demonstration datasets for imitation learning. However, most large-scale robotics datasets are collected in the real-world, often via manual teleoperation. This process is labor-intensive, slow, hardware-dependent, [...]
Advancing Spacecraft Autonomy: Optimal GNC, Vision-Based Estimation, and Systems Integration for Small Spacecraft
Abstract: Optimization and machine learning-based methods are increasingly critical in enhancing the autonomy, efficiency, and overall return on investment (ROI) of small, resource-constrained spacecraft. By enabling more effective decision-making, adaptive control, and robust state estimation, these techniques expand mission capabilities while operating within strict mass, power, and computational limitations. This thesis builds on previous contributions [...]
Human-System Communications for Expectation Mismatch
Abstract: Robots, and autonomous systems in general, are becoming increasingly more advanced beyond traditional functions. This can potentially widen the mismatch between human expectations of system behaviors during interaction, especially when the systems behave unexpectedly. Unexpected system behaviors could induce negative emotional responses in humans, which not all systems have the capability of recognizing and [...]
Integrating Safety Across the Learning-Based Perception Pipeline: From Training to Deployment
Abstract: Robots operating in safety-critical environments must reason under uncertainty and novel situations. However, recent advances in data-driven perception have made it challenging to provide formal safety guarantees, particularly when systems encounter out-of-distribution or previously unseen inputs. For such systems to be safely deployed in the real world, we need to incorporate safety considerations alongside [...]