130 citations · 339 across the 23 of their papers we have counts for
23 papers
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…
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…
Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons
Kejia Ren, Lydia E. Kavraki, Kaiyu Hang
Robot manipulation in cluttered environments often requires complex and sequential rearrangement of multiple objects in order to achieve the desired reconfiguration of the target o…
Synthesis from Satisficing and Temporal Goals
Suguman Bansal, Lydia Kavraki, Moshe Y. Vardi +1
Reactive synthesis from high-level specifications that combine hard constraints expressed in Linear Temporal Logic LTL with soft constraints expressed by discounted-sum (DS) reward…
Learning to Retrieve Relevant Experiences for Motion Planning
Constantinos Chamzas, Aedan Cullen, Anshumali Shrivastava +1
Recent work has demonstrated that motion planners' performance can be significantly improved by retrieving past experiences from a database. Typically, the experience database is q…
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…
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Constantinos Chamzas, Martina Lippi, Michael C. Welle +3
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the rec…
KDF: Kinodynamic Motion Planning via Geometric Sampling-based Algorithms and Funnel Control
Christos K. Verginis, Dimos V. Dimarogonas, Lydia E. Kavraki
We integrate sampling-based planning techniques with funnel-based feedback control to develop KDF, a new framework for solving the kinodynamic motion-planning problem via funnel co…
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…
Path Planning for Manipulation using Experience-driven Random Trees
Èric Pairet, Constantinos Chamzas, Yvan Petillot +1
Robotic systems may frequently come across similar manipulation planning problems that result in similar motion plans. Instead of planning each problem from scratch, it is preferab…
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…
LTLf Synthesis on Probabilistic Systems
Andrew M. Wells, Morteza Lahijanian, Lydia E. Kavraki +1
Many systems are naturally modeled as Markov Decision Processes (MDPs), combining probabilities and strategic actions. Given a model of a system as an MDP and some logical specific…
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
W. Cannon Lewis, Mark Moll, Lydia E. Kavraki
Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodele…
Using Local Experiences for Global Motion Planning
Constantinos Chamzas, Anshumali Shrivastava, Lydia E. Kavraki
Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to gene…
Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives
Yue Wang, Swarat Chaudhuri, Lydia E. Kavraki
Planning robust executions under uncertainty is a fundamental challenge for building autonomous robots. Partially Observable Markov Decision Processes (POMDPs) provide a standard f…
Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments
Muhayyuddin, Mark Moll, Lydia Kavraki +1
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstruct…
An Extensible Benchmarking Infrastructure for Motion Planning Algorithms
Mark Moll, Ioan A. Sucan, Lydia E. Kavraki
Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many vari…