Vladimír Petrík — accepted AI-conference papers

2026

Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion

Arthur Haffemayer, Alexandre Chapin, Armand Jordana, Krzysztof Wojciechowski, Florent Lamiraux, Nicolas Mansard +1

RA-L 2026

Acting in cluttered environments requires predicting and avoiding collisions while still achieving precise control. Conventional optimization-based controllers can enforce physical constraints, but they struggle to produce feasible solutions quickly when many obstacles are present. Diffusion models

Cited by 1 Source ↗Scholar ↗

2025

Temporally Consistent Object 6D Pose Estimation for Robot Control

Kateryna Zorina, Vojtech Priban, Mederic Fourmy, Josef Sivic, Vladimír Petrík

RA-L 2025

Single-view RGB object pose estimators have reached a level of precision and efficiency that makes them good candidates for vision-based robot control. However, off-the-shelf methods lack temporal consistency and robustness that are mandatory for a stable feedback control. In this work, we develop a

Cited by 4 Source ↗Scholar ↗

2022

Collision Detection Accelerated: An Optimization Perspective

Louis Montaut, Quentin Le Lidec, Vladimír Petrík, Josef Sivic, Justin Carpentier

RSS 2022poster

Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has been often tackled as a computational geometry problem, with the Gilbert, Johnson and Keerthi (GJK) algorithm being the most common approach today. In this work we show that co…

Cited by 30 Source ↗PDF ↗code ↗Scholar ↗

2022

Learning Object Manipulation Skills from Video via Approximate Differentiable Physics

Vladimír Petrík, Mohammad Nomaan Qureshi, Josef Sivic, Makar Tapaswi

IROS 2022poster

We aim to teach robots to perform simple object manipulation tasks by watching a single video demonstration. Towards this goal, we propose an optimization approach that outputs a coarse and temporally evolving 3D scene to mimic the action demonstrated in the input video. Similar to previous work, a…

Cited by 7 Source ↗Scholar ↗

2022

Learning to Manipulate Tools by Aligning Simulation to Video Demonstration

Kateryna Zorina, Justin Carpentier, Josef Sivic, Vladimír Petrík

RA-L 2022

A seamless integration of robots into human environments requires robots to learn how to use existing human tools. Current approaches for learning tool manipulation skills mostly rely on expert demonstrations provided in the target robot environment, for example, by manually guiding the robot manipu

Cited by 11 Source ↗Scholar ↗

2020

Learning Object Manipulation Skills via Approximate State Estimation from Real Videos

Vladimír Petrík, Makarand Tapaswi, Ivan Laptev, Josef Sivic

CoRL 2020

Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and error, or use expert demonstrations that are challenging to obtain. In this paper, we explore a method that facilitates lea

Cited by 0 Source ↗PDF ↗Scholar ↗

2019

Feedback-based Fabric Strip Folding

Vladimír Petrík, Ville Kyrki

IROS 2019poster

Accurate manipulation of a deformable body such as a piece of fabric is difficult because of its many degrees of freedom and unobservable properties affecting its dynamics. To alleviate these challenges, we propose the application of feedback-based control to robotic fabric strip folding. The feedba…

Cited by 24 Source ↗Scholar ↗

2018

Automatic Material Properties Estimation for the Physics-Based Robotic Garment Folding

Vladimír Petrík, Jakub Cmíral, Vladimír Smutný, Pavel Krsek, Václav Hlaváč

ICRA 2018poster

The estimation of the fabric material property during the folding is presented. The available techniques for the accurate garment folding rely on known material properties. Currently, the properties are estimated by an operator in advance of folding. We propose an iterative strategy, which updates t…

Cited by 5 Source ↗Scholar ↗

2017

Model-free approach to garments unfolding based on detection of folded layers

Jan Stria, Vladimír Petrík, Vaclav Hlaváč

IROS 2017poster

The proposed work deals with robotic unfolding of a garment that has been placed flat on a table and folded over a certain axis. The algorithm combines image and depth data to detect the bottom and top (folded) layer of the garment. The detection is formulated as a labeling of the garment surface an…

Cited by 11 Source ↗Scholar ↗

2016

Physics-based model of a rectangular garment for robotic folding

Vladimír Petrík, Vladimír Smutný, Pavel Krsek, Václav Hlaváč

IROS 2016poster

The ability to perform an accurate robotic fold is essential to obtain the properly folded garment. Available solutions rely on a rough folding surface or on a comprehensive simulation, both preventing the garment from slipping on the table during folding. This paper proposes a new algorithm for a f…

Cited by 21 Source ↗Scholar ↗