86 citations · 163 across the 21 of their papers we have counts for
15 papers · 1 filter
SYMBOLIZER: Symbolic Model-free Task Planning with VLMs
Sami Azirar, Zlatan Ajanovic, Hermann Blum
Traditional Task and Motion Planning (TAMP) systems depend on physics models for motion planning and discrete symbolic models for task planning. Although physics model are often av…
Sequentially Teaching Sequential Tasks : Teaching Robots Long-horizon Manipulation Skills
Zlatan Ajanović, Ravi Prakash, Leandro de Souza Rosa +1
Learning from demonstration has proved itself useful for teaching robots complex skills with high sample efficiency. However, teaching long-horizon tasks with multiple skills is ch…
Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives
Benjamin Kraljusic, Zlatan Ajanovic, Nermin Covic +1
This work proposes a motion planning algorithm for robotic manipulators that combines sampling-based and search-based planning methods. The core contribution of the proposed approa…
Mode Collapse Happens: Evaluating Critical Interactions in Joint Trajectory Prediction Models
Maarten Hugenholtz, Anna Meszaros, Jens Kober +1
Autonomous Vehicle decisions rely on multimodal prediction models that account for multiple route options and the inherent uncertainty in human behavior. However, models can suffer…
Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making
M. Youssef Abdelhamid, Lennart Vater, Zlatan Ajanovic
Developing decision-making algorithms for highly automated driving systems remains challenging, since these systems have to operate safely in an open and complex environments. Rein…
Search-based versus Sampling-based Robot Motion Planning: A Comparative Study
Georgios Sotirchos, Zlatan Ajanovic
Robot motion planning is a challenging domain as it involves dealing with high-dimensional and continuous search space. In past decades, a wide variety of planning algorithms have…
ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Runyu Ma, Jelle Luijkx, Zlatan Ajanovic +1
In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation…
A Multi-Heuristic Search-based Motion Planning for Automated Parking
Bhargav Adabala, Zlatan Ajanović
In unstructured environments like parking lots or construction sites, due to the large search-space and kinodynamic constraints of the vehicle, it is challenging to achieve real-ti…
Robotic Packaging Optimization with Reinforcement Learning
Eveline Drijver, Rodrigo Pérez-Dattari, Jens Kober +2
Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times. This work…
Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
Zlatan Ajanović, Enrico Regolin, Barys Shyrokau +3
To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achie…
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti +1
Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses).…
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari +7
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of…
Overview of Tools Supporting Planning for Automated Driving
Kailin Tong, Zlatan Ajanovic, Georg Stettinger
Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools…
Search-Based Motion Planning for Performance Autonomous Driving
Zlatan Ajanovic, Enrico Regolin, Georg Stettinger +2
Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference…
Search-based optimal motion planning for automated driving
Zlatan Ajanovic, Bakir Lacevic, Barys Shyrokau +2
This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of se…