20 citations · 28 across the 12 of their papers we have counts for
12 papers
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…
Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
Benedict Quartey, Sebastian Castro, Eric Rosen +3
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skil…
AORRTC: Almost-Surely Asymptotically Optimal Planning with RRT-Connect
Tyler Wilson, Wil Thomason, Zachary Kingston +1
Finding high-quality solutions quickly is an important objective in motion planning. This is especially true for high-degree-of-freedom robots. Satisficing planners have traditiona…
Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling
Yuanchu Liang, Edward Kim, Wil Thomason +3
Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalabil…
Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)
Tyler S. Wilson, Wil Thomason, Zachary Kingston +2
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. T…
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 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…
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…
Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning
Wil Thomason, Marlin P. Strub, Jonathan D. Gammell
High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms sol…
Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions
Wil Thomason, Hadas Kress-Gazit
Integrated Task and Motion Planning (TMP) provides a promising class of approaches for solving robot planning problems with intricate symbolic and geometric constraints. However, t…
Ensuring Progress for Multiple Mobile Robots via Space Partitioning, Motion Rules, and Adaptively Centralized Conflict Resolution
Claire Liang, Wil Thomason, E. Andy Ricci +1
In environments where multiple robots must coordinate in a shared space, decentralized approaches allow for decoupled planning at the cost of global guarantees, while centralized a…