12 papers
PEEL: Parallel Extraction for Long-Horizon Disassembly Planning via Scale-Invariant Sampling
Servet B. Bayraktar, Andreas Orthey, Zachary Kingston +1
Long-horizon multi-part object disassembly requires robots to compute feasible sequences of collision-free removal motions, even in the presence of tight, narrow escape corridors.…
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized plan…
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
Qingyi Chen, Zachary Kingston, Ahmed H. Qureshi
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized plan…
Think Fast and Far: Long-Horizon Online POMDP Planning via Rapid State Sampling
Yuanchu Liang, Edward Kim, J. Arden Knoll +4
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
The Open Motion Planning Library 2.0
Weihang Guo, Theodoros Tyrovouzis, Emiliano Flores +5
The Open Motion Planning Library (OMPL), first released in 2008, has become a cornerstone of the motion planning community, providing implementations of a wide range of state-of-th…
Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness
Thai Duong, Clayton W. Ramsey, Zachary Kingston +2
Motion planning under dynamics constraints, i.e, kinodynamic planning, enables safe robot operation by generating dynamically feasible trajectories that the robot can accurately tr…