130 citations · 339 across the 23 of their papers we have counts for
7 papers · 1 filter
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…
Robots as AI Double Agents: Privacy in Motion Planning
Rahul Shome, Zachary Kingston, Lydia E. Kavraki
Robotics and automation are poised to change the landscape of home and work in the near future. Robots are adept at deliberately moving, sensing, and interacting with their environ…
The Planner Optimization Problem: Formulations and Frameworks
Yiyuan Lee, Katie Lee, Panpan Cai +2
Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the p…
Object Reconfiguration with Simulation-Derived Feasible Actions
Yiyuan Lee, Wil Thomason, Zachary Kingston +1
3D object reconfiguration encompasses common robot manipulation tasks in which a set of objects must be moved through a series of physically feasible state changes into a desired f…
Kinodynamic Rapidly-exploring Random Forest for Rearrangement-Based Nonprehensile Manipulation
Kejia Ren, Podshara Chanrungmaneekul, Lydia E. Kavraki +1
Rearrangement-based nonprehensile manipulation still remains as a challenging problem due to the high-dimensional problem space and the complex physical uncertainties it entails. W…