56 citations · 64 across the 23 of their papers we have counts for
24 papers
SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation
Kaivalya Agrawal, Md Ashiqur Rahman, Raymond A. Yeh +1
Vision-Language-Action (VLA) models are a class of generalist robot policies that map camera images and language instructions directly to robot actions. While promising, these mode…
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…
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…
Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control
Shrutheesh R Iyer, I-Chia Chang, Andrew Z. Liu +2
Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorith…
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…
Using VLM Reasoning to Constrain Task and Motion Planning
Muyang Yan, Miras Mengdibayev, Ardon Floros +3
In task and motion planning, high-level task planning is done over an abstraction of the world to enable efficient search in long-horizon robotics problems. However, the feasibilit…
Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots
Qingxi Meng, Emiliano Flores, Carlos Quintero-Peña +5
Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad r…
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…
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…
Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation
Weihang Guo, Zachary Kingston, Kaiyu Hang +1
Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construc…
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…
CaStL: Constraints as Specifications through LLM Translation for Long-Horizon Task and Motion Planning
Weihang Guo, Zachary Kingston, Lydia E. Kavraki
Large Language Models (LLMs) have demonstrated remarkable ability in long-horizon Task and Motion Planning (TAMP) by translating clear and straightforward natural language problems…
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…
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…
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…
Solving Rearrangement Puzzles using Path Defragmentation in Factored State Spaces
Servet B. Bayraktar, Andreas Orthey, Zachary Kingston +2
Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we d…
MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
Constantinos Chamzas, Carlos Quintero-Peña, Zachary Kingston +5
Recently, there has been a wealth of development in motion planning for robotic manipulation new motion planners are continuously proposed, each with their own unique strengths and…
Robowflex: Robot Motion Planning with MoveIt Made Easy
Zachary Kingston, Lydia E. Kavraki
Robowflex is a software library for robot motion planning in industrial and research applications, leveraging the popular MoveIt library and Robot Operating System (ROS) middleware…
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
Constantinos Chamzas, Zachary Kingston, Carlos Quintero-Peña +2
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries. However, for robots with m…