Josip Josifovski — accepted AI-conference papers

2024

Continual Domain Randomization

Josip Josifovski, Sayantan Auddy, Mohammadhossein Malmir, Justus Piater, Alois Knoll, Nicolás Navarro-Guerrero

IROS 2024poster

Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tunable parameters from the start of the training, from which the parameters are randomized simultaneously to train a robus…

Cited by 2 Source ↗code ↗Scholar ↗

2022

Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks

Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann, Bare Luka Žagar, Nicolás Navarro-Guerrero, Alois Knoll

IROS 2022poster

Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different random…

Cited by 13 Source ↗Scholar ↗

2018

Object Detection and Pose Estimation Based on Convolutional Neural Networks Trained with Synthetic Data

Josip Josifovski, Matthias Kerzel, Christoph Pregizer, Lukas Posniak, Stefan Wermter

IROS 2018poster

Instance-based object detection and fine pose estimation is an active research problem in computer vision. While the traditional interest-point-based approaches for pose estimation are precise, their applicability in robotic tasks relies on controlled environments and rigid objects with detailed tex…

Cited by 51 Source ↗Scholar ↗