19 citations · 24 across the 11 of their papers we have counts for
11 papers
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…
BeBOP -- Combining Reactive Planning and Bayesian Optimization to Solve Robotic Manipulation Tasks
Jonathan Styrud, Matthias Mayr, Erik Hellsten +2
Robotic systems for manipulation tasks are increasingly expected to be easy to configure for new tasks. While in the past, robot programs were often written statically and tuned ma…
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…
SkiROS2: A skill-based Robot Control Platform for ROS
Matthias Mayr, Francesco Rovida, Volker Krueger
The need for autonomous robot systems in both the service and the industrial domain is larger than ever. In the latter, the transition to small batches or even "batch size 1" in pr…
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…
A C++ Implementation of a Cartesian Impedance Controller for Robotic Manipulators
Matthias Mayr, Julian M. Salt-Ducaju
Cartesian impedance control is a type of motion control strategy for robots that improves safety in partially unknown environments by achieving a compliant behavior of the robot wi…
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…
Learning Skill-based Industrial Robot Tasks with User Priors
Matthias Mayr, Carl Hvarfner, Konstantinos Chatzilygeroudis +2
Robot skills systems are meant to reduce robot setup time for new manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often difficult to find the right skill paramet…
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…