activity
20222025
most citedCombining Planning, Reasoning and Reinforcement Learning to solve Industrial Robot Tasks

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO20251 cited

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…

cs.RO2024

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…

cs.RO2024

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…

cs.RO2023

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…

cs.RO2023

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…

cs.RO20222 cited

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…