19 citations · 31 across the 11 of their papers we have counts for
11 papers
A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees
Faseeh Ahmad, Hashim Ismail, Jonathan Styrud +2
Robotic systems often face execution failures due to unexpected obstacles, sensor errors, or environmental changes. Traditional failure recovery methods rely on predefined strategi…
Addressing Failures in Robotics using Vision-Based Language Models (VLMs) and Behavior Trees (BT)
Faseeh Ahmad, Jonathan Styrud, Volker Krueger
In this paper, we propose an approach that combines Vision Language Models (VLMs) and Behavior Trees (BTs) to address failures in robotics. Current robotic systems can handle known…
Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management
Faseeh Ahmad, Matthias Mayr, Sulthan Suresh-Fazeela +1
In dynamic operational environments, particularly in collaborative robotics, the inevitability of failures necessitates robust and adaptable recovery strategies. Traditional automa…
Flexible and Adaptive Manufacturing by Complementing Knowledge Representation, Reasoning and Planning with Reinforcement Learning
Matthias Mayr, Faseeh Ahmad, Volker Krueger
This paper describes a novel approach to adaptive manufacturing in the context of small batch production and customization. It focuses on integrating task-level planning and reason…
Using Knowledge Representation and Task Planning for Robot-agnostic Skills on the Example of Contact-Rich Wiping Tasks
Matthias Mayr, Faseeh Ahmad, Alexander Duerr +1
The transition to agile manufacturing, Industry 4.0, and high-mix-low-volume tasks require robot programming solutions that are flexible. However, most deployed robot solutions are…
Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations
Faseeh Ahmad, Matthias Mayr, Volker Krueger
The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior…
Combining Planning, Reasoning and Reinforcement Learning to solve Industrial Robot Tasks
Matthias Mayr, Faseeh Ahmad, Konstantinos Chatzilygeroudis +2
One of today's goals for industrial robot systems is to allow fast and easy provisioning for new tasks. Skill-based systems that use planning and knowledge representation have long…
Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration
Matthias Mayr, Faseeh Ahmad, Konstantinos Chatzilygeroudis +2
In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handl…
Learning of Parameters in Behavior Trees for Movement Skills
Matthias Mayr, Konstantinos Chatzilygeroudis, Faseeh Ahmad +2
Reinforcement Learning (RL) is a powerful mathematical framework that allows robots to learn complex skills by trial-and-error. Despite numerous successes in many applications, RL…
The arithmetic basilica: a quadratic PCF arboreal Galois group
Faseeh Ahmad, Robert L. Benedetto, Jennifer Cain +2
The arboreal Galois group of a polynomial over a field encodes the action of Galois on the iterated preimages of a root point , analogous to the action of Galois…
A Formal Framework for Robot Construction Problems: A Hybrid Planning Approach
Faseeh Ahmad, Esra Erdem, Volkan Patoglu
We study robot construction problems where multiple autonomous robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ram…