July
2026
Exploiting Structure for Real-Time Robot Motion Planning and Control
Authors:
Abstract:
Real-time robot motion in dynamic and uncertain environments requires algorithms that can make effective decisions under strict computational constraints. High-fidelity dynamics, large search spaces, uncertainty over future outcomes, and the pursuit of optimal motion each impose substantial computational costs. These challenges become especially pronounced in partially observable and rapidly changing environments, where a robot must continually update its predictions and react before a previously computed plan becomes obsolete. This thesis presents two model-based frameworks that exploit structure to bridge deliberative
planning and reactive control, enabling efficient closed-loop execution without solving a full
planning problem at every step.
First, we study projectile interception, where a robot must begin moving after only a few noisy measurements of a fast-moving object’s trajectory. Our framework couples uncertainty estimates from an RGB-D tracking system with a sparse kinodynamic graph of executable motion primitives. As the projectile estimate evolves, the system rapidly reevaluates candidate motions and executes partial trajectories that remain robust across a distribution of possible future outcomes. This approach maintains millisecond-scale replanning and improves interception performance.
The second framework learns the parameters of convex optimization-based safety filters formulated as control barrier function quadratic programs (CBF-QPs). Imitation learning transfers goal-directed behavior from a computationally expensive global planner into a local reactive controller while retaining explicit model-based safety constraints. This combination
shifts computation from online global search to offline learning, enabling efficient closed-loop execution with reduced planning effort. We show that, under matched online computational budgets, the learned safety filter improves performance in planar navigation environments with moving obstacles and in constrained manipulator-planning tasks.
Together, these contributions show how structured representations can preserve reasoning about dynamics, uncertainty, and safety while enabling efficient real-time robot motion.
planning and reactive control, enabling efficient closed-loop execution without solving a full
planning problem at every step.
First, we study projectile interception, where a robot must begin moving after only a few noisy measurements of a fast-moving object’s trajectory. Our framework couples uncertainty estimates from an RGB-D tracking system with a sparse kinodynamic graph of executable motion primitives. As the projectile estimate evolves, the system rapidly reevaluates candidate motions and executes partial trajectories that remain robust across a distribution of possible future outcomes. This approach maintains millisecond-scale replanning and improves interception performance.
The second framework learns the parameters of convex optimization-based safety filters formulated as control barrier function quadratic programs (CBF-QPs). Imitation learning transfers goal-directed behavior from a computationally expensive global planner into a local reactive controller while retaining explicit model-based safety constraints. This combination
shifts computation from online global search to offline learning, enabling efficient closed-loop execution with reduced planning effort. We show that, under matched online computational budgets, the learned safety filter improves performance in planar navigation environments with moving obstacles and in constrained manipulator-planning tasks.
Together, these contributions show how structured representations can preserve reasoning about dynamics, uncertainty, and safety while enabling efficient real-time robot motion.