Publikationen
Hier findest du eine Übersicht meiner wissenschaftlichen Beiträge. Eine Liste meiner Publikationen gibt es auch auf Google Scholar und ORCID.
Abstract
Industrial robots are ubiquitous in today’s
production systems. Here, strict occupational health and safety
regulations apply to their operation. As a rule, robots work
spatially separated from humans. Only special designed robots
are currently allowed to work directly with humans at
workplaces in a human-robot collaboration (HRC) setup.
HRC can enable companies to meet the challenges posed by
the growing complexity of production processes and the need for
flexibilization with the aim of increasing efficiency. To achieve
these goals, however, humans and robots must be able to work
together trustworthy and without fear. Although robots capable
for HRC are subject to limitations in terms of allowed speed and
load capacity to ensure safe operation, it is not yet known what
level of fear and trust enables efficient HRC for the human
worker. Here, a transparent interaction design enabling the
prediction of robot’s movement and intentions can help to ensure
a smooth and seamless HRC.
Despite many different approaches to achieve transparency in
HRC, it is still unclear which transparency mechanisms are best
suited for different types of robots and situations, especially
when humans have different levels of attention towards the
robot. The overall goal of our work is to contribute towards more
comprehensible and trustworthy HRC interaction. This leads to
a more flexible and human-centered implementation of robots in
production. In this paper, we’ll give a brief overview of relevant
literature and a comprehensive outlook on our future research.
Abstract
The field of robotics is currently experiencing a
new boom, driven in part by the hype surrounding generative
AI. This paper provides an insight into how Large Language
Models (LLMs) can enhance communication between robots
and humans, making interactions more interactive and intuitive.
Additionally, it explores how a robotic system can autonomously
solve abstract tasks with the help of AI-Agents, focusing
specifically on construction-related challenges as examples
building structures with Lego stones.
Abstract
Autonomous mobile robots (AMRs) and mobile
manipulators are critical components in modern logistics and
manufacturing. However, their efficiency depends on the timely
detection and resolution of anomalies to prevent downtime.
Traditional predictive maintenance systems often require large
data sets and cloud connectivity, which makes them unsuitable
for some environments. The IntelliVoiceAnalytic project
addresses these challenges by developing an onboard AI-based
anomaly detection system that can operate with minimal
training data. The solution integrates offline-capable speech
recognition and synthesis tools to enable intuitive speech-based
human-robot interaction. This paper presents the project
approach, including data acquisition, synthetic anomaly
generation, and development of a demonstrator AMR.
Abstract
In this work, we present a new method of learning
a representation and a control policy from expert demonstrations. We approximate geodesics on the demonstration manifold
based on the position of fitted prototypes and their connectivity
representing the data topology. A first experiment is also
conducted and the results are reported.
Kaden, S., Schwarz, L., & Röhrbein, F. (2024, August). A Research Platform for Human-Robot-Interaction with Focus on Collaborative Assembly Scenarios. In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN) (pp. 1436-1442). IEEE.
Abstract
Effective communication between robots and human operators is crucial for seamless collaboration in industrial settings. We aim to capture the requirements of collaborative assembly procedures in the context of the Industry 5.0 paradigm and explore the feasibility of various multi-modal feedback systems, including visual cues, sound effects, and virtual eyes. To achieve this, we present a novel research platform designed to investigate human-robot interaction (HRI) strategies in collaborative assembly tasks. The platform uses LEGO bricks to simulate real-world assembly processes. An experiment is designed in which human and robot collaboratively build a structure, allowing us to investigate potential communication interfaces between them. A preliminary user study provides first insights into the perception of the robot’s visualized intentions and actions by the user. The platform setup is not intended to be a fixed system, but rather a starting point for further investigation and future studies in the field of HRI.
Abstract
This paper describes a short-term qualitative pilot study with group workshops in Germany. Together with elderly people, we are exploring their ideas and wishes for robots in a future home care setting.
Abstract
Natural and human-like arm motions are promising features to facilitate social understanding of humanoid robots. To this end, we integrate biophysical characteristics of human arm-motions into sampling-based motion planning. We show the generality of our method by evaluating it with multiple manipulators. Our first contribution is to introduce a set of cost functions to optimize for human-like arm postures during collision-free motion planning. In a subsequent step, an optimization phase is used to improve the human-likeness of the initial path. Additionally, we present an interpolation approach for generating obstacle-aware and multi-modal velocity profiles. We thus generate collision-free and human-like motions in narrow passages while allowing for natural acceleration in free space.
Abstract
Sampling-based motion planning algorithms have been continuously developed for more than two decades. Apart from mobile robots, they are also widely used in manipulator motion planning. Hence, these methods play a key role in collaborative and shared workspaces. Despite numerous improvements, their performance can highly vary depending on the chosen parameter setting. The optimal parameters depend on numerous factors such as the start state, the goal state and the complexity of the environment. Practitioners usually choose these values using their experience and tedious trial and error experiments. To address this problem, recent works combine hyperparameter optimization methods with motion planning. They show that tuning the planner’s parameters can lead to shorter planning times and lower costs. It is not clear, however, how well such approaches generalize to a diverse set of planning problems that include narrow passages as well as barely cluttered environments. In this work, we analyze optimized planner settings for a large set of diverse planning problems. We then provide insights into the connection between the characteristics of the planning problem and the optimal parameters. As a result, we provide a list of recommended parameters for various use-cases. Our experiments are based on a novel motion planning benchmark for manipulators which we provide at https://mytuc.org/rybj.
Abstract
A major task in motion planning is to find paths that have a high ability to react to external influences while ensuring a collision-free operation at any time. This flexibility is even more important in human-robot collaboration since unforeseen events can occur anytime. Such ability can be described as mobility, which is composed of two characteristics. First, the ability to manipulate, and second, the distance to joint limits. This mobility needs to be optimized while generating collision-free motions so that there is always the flexibility of the robot to evade dynamic obstacles in the future execution of generated paths. For this purpose, we present a Rapidly-exploring Random Tree (RRT), which applies additional costs and sampling methods to increase mobility. Additionally, we present two methods for the optimization of a generated path. Our first approach utilizes the built-in capabilities of the RRT*. The second method optimize the path with the stochastic trajectory optimization for motion planning (STOMP) approach with Gaussian Mixture Models. Moreover, we evaluate the algorithms in complex simulation and real environments and demonstrate an enhancement of mobility.
Abstract
Natural and human-like arm motions are promising features to facilitate social understanding of humanoid robots. To this end, we integrate biophysical characteristics of human arm-motions into sampling-based motion planning. We show the generality of our method by evaluating it with multiple manipulators. Our first contribution is to introduce a set of cost functions to optimize for human-like arm postures during collision-free motion planning. In a subsequent step, an optimization phase is used to improve the human-likeness of the initial path. Additionally, we present an interpolation approach for generating obstacle-aware and multi-modal velocity profiles. We thus generate collision-free and human-like motions in narrow passages while allowing for natural acceleration in free space.
Abstract
A major task in motion planning is to find suitable movements with large manipulability, while collision-free operation must be guaranteed. This condition is increasingly important in the collaboration between humans and robots, as the capability of avoidance to humans or dynamic obstacles must be ensured anytime. For this purpose, paths in motion planning have to be optimized with respect to manipulability and distance to obstacles. Because with a large manipulability the robot has at any time, the possibility of evading due to the greater freedom of movement. Alternatively, the robot can be pushed away by using a Cartesian impedance control. To achieve this, we have developed a combined approach. First, we introduce a Rapidly-exploring Random Tree, which is extended and optimized by state costs for manipulability. Secondly, we perform an optimization using the STOMP method and Gaussian Mixture Models. With this dual approach we are able to find paths in narrow passages and simultaneously optimize the path in terms of manipulability.