56 citations · 143 across the 20 of their papers we have counts for
23 papers
Asymmetric Scout-Worker Reconnaissance for Route Validation in Unknown Environments
Kashif Khurshid Noori, Jaskrit Singh, Athanasios Ch. Kapoutsis +2
This paper studies asymmetric scout-worker reconnaissance in unknown environments, where a small, agile autonomous scout explores routes for a larger worker robot that must visit a…
RoboMonitor: Label-Efficient Runtime Monitoring of Robot Task Execution via Predictive Representation Learning
Abhiroop Ajith, Gokul Narayanan, Kyle Coelho +7
Learned robot policies produce actions, but their outputs alone do not establish whether execution is progressing as intended. Robot execution monitoring requires identifying the c…
DGT-Map: Directional Global Traversability Mapping Utilizing Multi-Task Learning for Heterogeneous Vehicles
Jaskrit Singh, Kashif K. Noori, Jing Xiao +1
Off-road traversability is direction-dependent and vehicle specific, yet most global maps assign a single isotropic cost to each location. Existing learned estimators are also comm…
MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption
Donghyung Lee, Seyedali Golestaneh, Jaskrit Singh +3
Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distribution, that cannot be inferred…
STRIPS-WM: Learning Grounded Propositional STRIPS-style World Models from Images
Abhiroop Ajith, Constantinos Chamzas
Robots performing long-horizon visual manipulation observe high-dimensional images, but successful plans depend on action-relevant facts: what can be done now and what changes afte…
AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems
Seyedali Golestaneh, Zhuoyun Zhong, Donghyung Lee +1
Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or non-holonomic systems. Howeve…
Terminal Matters: Kinodynamic Planning with a Terminal Cost and Learned Uncertainty in Belief State-Cost Space
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
In many real-world robotic tasks, robots must generate dynamically feasible motions that reliably reach desired goals even under uncertainty. Yet existing sampling-based kinodynami…
CoAd: Constant-Time Planning for Continuous Goal Manipulation with Compressed Library and Online Adaptation
Adil Shiyas, Zhuoyun Zhong, Constantinos Chamzas
In many robotic manipulation tasks, the robot repeatedly solves motion-planning problems that differ mainly in the location of the goal object and its associated obstacle, while th…
COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments
Niranjan Kumar Ilampooranan, Constantinos Chamzas
The ability to solve motion-planning queries within a fixed time budget is critical for deploying robotic systems in time-sensitive applications. Semi-static environments, where mo…
Learning Discrete Abstractions for Visual Rearrangement Tasks Using Vision-Guided Graph Coloring
Abhiroop Ajith, Constantinos Chamzas
Learning abstractions directly from data is a core challenge in robotics. Humans naturally operate at an abstract level, reasoning over high-level subgoals while delegating executi…
ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas
Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile settings such as pushing or rolling, where…
Multi-layer Motion Planning with Kinodynamic and Spatio-Temporal Constraints
Jeel Chatrola, Abhiroop Ajith, Kevin Leahy +1
We propose a novel, multi-layered planning approach for computing paths that satisfy both kinodynamic and spatiotemporal constraints. Our three-part framework first establishes pot…
Image-Based Roadmaps for Vision-Only Planning and Control of Robotic Manipulators
Sreejani Chatterjee, Abhinav Gandhi, Berk Calli +1
This work presents a motion planning framework for robotic manipulators that computes collision-free paths directly in image space. The generated paths can then be tracked using vi…
Expansion-GRR: Efficient Generation of Smooth Global Redundancy Resolution Roadmaps
Zhuoyun Zhong, Zhi Li, Constantinos Chamzas
Global redundancy resolution (GRR) roadmaps is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable…
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…
Meta-Policy Learning over Plan Ensembles for Robust Articulated Object Manipulation
Constantinos Chamzas, Caelan Garrett, Balakumar Sundaralingam +2
Recent work has shown that complex manipulation skills, such as pushing or pouring, can be learned through state-of-the-art learning based techniques, such as Reinforcement Learnin…
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…
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…
cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope
Dimitrios Chamzas, Constantinos Chamzas, Konstantinos Moustakas
During the last years, the emerging field of Augmented & Virtual Reality (AR-VR) has seen tremendousgrowth. At the same time there is a trend to develop low cost high-quality AR sy…
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…
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…