Quantum-Weighted Motion Planning for Robotic Manipulators
Jesse Beneventi, Sihui Li
Jesse’s research investigates how quantum computing can assist robotic motion planning by improving sampling within discretized configuration spaces. The current project discretizes continuous configuration space into a collision-labeled grid for 3D manipulator motion planning, enabling systematic identification of collision-free and invalid regions during planning. This approach uses grid-based configuration-space search rather than planning directly in continuous space, allowing valid and invalid configurations to be identified in advance and sampled in a more structured manner. Building on prior quantum rapid-exploring random tree concepts, this work studies how quantum amplitude amplification can bias sampling toward useful regions of the search space during motion planning. Prior work in this area suggests that quantum search can be incorporated into sampling-based planners, including variants designed to improve exploration speed and parallel formulations intended to recover multiple useful samples from a shared database. The present research focuses on evaluating whether this quantum-weighted sampling strategy can improve planning efficiency in practice. Experiments are conducted using both simulated quantum hardware and real quantum hardware to compare idealized and device-limited behavior in the sampling process. The work began with a controlled 2D benchmark and is, at the time of writing, being extended toward higher-dimensional robotic manipulators. An additional research direction examines whether database-construction strategies inspired by annealing can improve exploration by promoting broader search early in planning and denser refinement later in the process.
•May 14, 2026•0 citations