5 papers
An Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility
Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto
We present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot's configuration space to…
Locally Optimal Solutions to Constraint Displacement Problems via Path-Obstacle Overlaps
Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto
We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stag…
A Framework for Task and Motion Planning based on Expanding AND/OR Graphs
Fulvio Mastrogiovanni, Antony Thomas
Robot autonomy in space environments presents unique challenges, including high perception and motion uncertainty, strict kinematic constraints, and limited opportunities for human…
Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM
Nicholas Attolino, Alessio Capitanelli, Fulvio Mastrogiovanni
PDDL-based symbolic task planning remains pivotal for robot autonomy yet struggles with dynamic human-robot collaboration due to scalability, re-planning demands, and delayed plan…
A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks
Hossein Karami, Antony Thomas, Fulvio Mastrogiovanni
In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we lev…