86 citations · 163 across the 21 of their papers we have counts for
23 papers
Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
Leon Hamm, Zlatan Ajanovic
Although advances such as chain-of-thought, tree-of-thought or reinforcement learning have improved the performance of LLMs in reasoning and planning tasks, they are still brittle…
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…
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning
Jelle Luijkx, Zlatan Ajanović, Laura Ferranti +1
Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ…
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…
Good Practices for Institutional Organization of Research Institutes: Excellence in Research and Positive Impact on Society
Zlatan Ajanović, Hamza Merzić, Suad Krilasević +10
In this paper, we analyze examples of research institutes that stand out in scientific excellence and social impact. We define key practices for evaluating research results, econom…
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…
SLOPE: Search with Learned Optimal Pruning-based Expansion
Davor Bokan, Zlatan Ajanovic, Bakir Lacevic
Heuristic search is often used for motion planning and pathfinding problems, for finding the shortest path in a graph while also promising completeness and optimal efficiency. The…
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…
Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals
Zlatan Ajanović, Emina Aličković, Aida Branković +7
Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide…
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…
A novel approach to model exploration for value function learning
Zlatan Ajanovic, Halil Beglerovic, Bakir Lacevic
Planning and Learning are complementary approaches. Planning relies on deliberative reasoning about the current state and sequence of future reachable states to solve the problem.…
Safe learning-based optimal motion planning for automated driving
Zlatan Ajanovic, Bakir Lacevic, Georg Stettinger +2
This paper presents preliminary work on learning the search heuristic for the optimal motion planning for automated driving in urban traffic. Previous work considered search-based…
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…
A novel model-based heuristic for energy optimal motion planning for automated driving
Zlatan Ajanovic, Michael Stolz, Martin Horn
Predictive motion planning is the key to achieve energy-efficient driving, which is one of the main benefits of automated driving. Researchers have been studying the planning of ve…