Josip Josifovski
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View article: Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics Open
Domain randomization has emerged as a fundamental technique in reinforcement learning (RL) to facilitate the transfer of policies from simulation to real-world robotic applications. Many existing domain randomization approaches have been p…
View article: DiAReL: Reinforcement Learning With Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
DiAReL: Reinforcement Learning With Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control Open
View article: Continual Domain Randomization
Continual Domain Randomization Open
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 …
View article: State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping
State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping Open
Choosing an appropriate representation of the environment for the underlying decision-making process of the reinforcement learning agent is not always straightforward. The state representation should be inclusive enough to allow the agent …
View article: DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control Open
Delayed Markov decision processes (DMDPs) fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance on these state augmentations, delay-resolved reinforcement l…
View article: Visuo-haptic object perception for robots: an overview
Visuo-haptic object perception for robots: an overview Open
The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologie…
View article: Optimising Trajectories in Simulations with Deep Reinforcement Learning for Industrial Robots in Automotive Manufacturing
Optimising Trajectories in Simulations with Deep Reinforcement Learning for Industrial Robots in Automotive Manufacturing Open
This paper outlines the concept of optimising trajectories for industrial robots by applying deep reinforcement learning in simulations. An application of high technical relevance is considered in a production line of an automotive manufac…
View article: Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control Open
View article: Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks Open
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 rob…
View article: Visuo-Haptic Object Perception for Robots: An Overview
Visuo-Haptic Object Perception for Robots: An Overview Open
The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologie…
View article: Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control Open
State-of-the-art reinforcement learning algorithms predominantly learn a policy from either a numerical state vector or images. Both approaches generally do not take structural knowledge of the task into account, which is especially preval…
View article: AI4DI Deliverable D2.4 Report on hybrid intelligent system and sub-system level modelling and simulation for integration of AI methods
AI4DI Deliverable D2.4 Report on hybrid intelligent system and sub-system level modelling and simulation for integration of AI methods Open
The present document is a deliverable of the AI4DI project, which is co-funded by the ECSEL Joint Undertaking under grant agreement No. 826060 and ECSEL JU Member States. This report gives an overview of the simulation and modelling approa…
View article: Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization
Robotic Information Gathering With Reinforcement Learning Assisted by Domain Knowledge: An Application to Gas Source Localization Open
Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for locali…
View article: Continual Learning on Incremental Simulations for Real-World Robotic Manipulation Tasks
Continual Learning on Incremental Simulations for Real-World Robotic Manipulation Tasks Open
Current state-of-the-art approaches for transferring deep-learning models trained in simulation either rely on highly realistic simulations or employ randomization techniques to bridge the reality gap. However, such strategies do not scale…
View article: Supplementary Material for "A Robotic Home Assistant with Memory Aid Functionality"
Supplementary Material for "A Robotic Home Assistant with Memory Aid Functionality" Open
This directory contains the questionnaire created for and the data collected during the user study conducted for the paper "A Robotic Home Assistant with Memory Aid Functionality", published at the KI 2016.DatasetThe collected data …
View article: Dataset for "A Robotic Home Assistant with Memory Aid Functionality"
Dataset for "A Robotic Home Assistant with Memory Aid Functionality" Open
This directory contains the questionnaire created for and the data collected during the user study conducted for the paper "A Robotic Home Assistant with Memory Aid Functionality", published at the KI 2016.DatasetThe collected data …