4 citations · 6 across the 11 of their papers we have counts for
10 papers · 1 filter
Task and Motion Planning for Execution in the Real
Tianyang Pan, Rahul Shome, Lydia E. Kavraki
Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reason…
Collision-Affording Point Trees: SIMD-Amenable Nearest Neighbors for Fast Collision Checking
Clayton W. Ramsey, Zachary Kingston, Wil Thomason +1
Motion planning against sensor data is often a critical bottleneck in real-time robot control. For sampling-based motion planners, which are effective for high-dimensional systems…
Stochastic Games for Interactive Manipulation Domains
Karan Muvvala, Andrew M. Wells, Morteza Lahijanian +2
As robots become more prevalent, the complexity of robot-robot, robot-human, and robot-environment interactions increases. In these interactions, a robot needs to consider not only…
Stochastic Implicit Neural Signed Distance Functions for Safe Motion Planning under Sensing Uncertainty
Carlos Quintero-Peña, Wil Thomason, Zachary Kingston +2
Motion planning under sensing uncertainty is critical for robots in unstructured environments to guarantee safety for both the robot and any nearby humans. Most work on planning un…
Motions in Microseconds via Vectorized Sampling-Based Planning
Wil Thomason, Zachary Kingston, Lydia E. Kavraki
Modern sampling-based motion planning algorithms typically take between hundreds of milliseconds to dozens of seconds to find collision-free motions for high degree-of-freedom prob…
Sampling-Based Motion Planning: A Comparative Review
Andreas Orthey, Constantinos Chamzas, Lydia E. Kavraki
Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date…