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This thesis studied how to achieve fluent human-robot teaming by perceiving task progress and recognizing human intentions during cooperative assembly. It began with two empirical human-human teaming studies to characterize communication mechanisms and intention prediction for assembly tasks, then developed a demonstrator using a robot arm, an assembly station, and a depth camera; key methods included a Petri-net-based task-state tracking algorithm using object detection and occlusion data, and a neural-network intention prediction module that uses spatial features for real-time per-user training. The evaluations reported robustness and efficiency of the tracking algorithm under non-deterministic executions, and an accuracy comparison of intention prediction against traditional action prediction approaches using interaction data. The paper’s limitations are that it focuses on assembly tasks in a specific demonstrator setting and does not describe application beyond this context. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
The market share of collaborative robots in the industry continues to grow steadily. However, there is still a need to improve human-robot collaboration further to support the ongoing industrial transformation, enabling robots to take on more tasks and function as true teammates. This requires advancements in both perception and decision-making capabilities. This thesis contributes to two key aspects of achieving seamless human-robot collaboration: perceiving task progress and recognising human intentions, all within a framework of flexible and fluent cooperation. The starting point involves two empirical studies on human-human teaming to identify communication mechanisms and intention prediction capabilities for assembly tasks. The findings have resulted in the development of a research demonstrator for a cooperative assembly station, with a particular emphasis on the flexible allocation of task steps and the integration of intention prediction. The prototype hardware includes a robot arm, an assembly station, and a depth camera. Core innovations are the task-state tracking algorithm and the intention prediction component. The task-state tracking algorithm is based on object detection and occlusion data from the camera. It models task execution in terms of the markings of a Petri net. The provided Petri net encodes all possible ways to execute the task in a space-efficient manner. Comprehensive evaluations demonstrate the algorithm's robustness and efficiency in non-deterministic task executions. The sequence of task steps executed by the human serves as input to the intention prediction module, which predicts the next steps using a neural network with a custom-designed feature space. This feature space encodes spatial information to enable efficient, real-time training for each user interacting with the robot. A comprehensive evaluation compares the accuracy of the module with traditional action prediction approaches using data from user interactions with the system. Finally, this thesis presents a comprehensive study on the system. Based on intention prediction, different robot behaviours are implemented and evaluated by a user study in terms of fluency and productivity questionnaires. Post-hoc analysis provides insights into the interrelationships and effects of robot behaviour on these measures. In summary, this thesis contributes technical foundations, empirical evaluations, and motivates further investigations into fluent and flexible human-robot teaming with intention prediction.
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Published July 1, 2025
| Version 0.9.0
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Open
Fluency in Dynamic Human-Robot Teaming with Intention Prediction - Main Application
Description
The market share of collaborative robots in the industry continues to grow steadily. However, there is still a need to improve human-robot collaboration further to support the ongoing industrial transformation, enabling robots to take on more tasks and function as true teammates. This requires advancements in both perception and decision-making capabilities. This thesis contributes to two key aspects of achieving seamless human-robot collaboration: perceiving task progress and recognising human intentions, all within a framework of flexible and fluent cooperation.
The starting point involves two empirical studies on human-human teaming to identify communication mechanisms and intention prediction capabilities for assembly tasks. The findings have resulted in the development of a research demonstrator for a cooperative assembly station, with a particular emphasis on the flexible allocation of task steps and the integration of intention prediction.
The prototype hardware includes a robot arm, an assembly station, and a depth camera. Core innovations are the task-state tracking algorithm and the intention prediction component. The task-state tracking algorithm is based on object detection and occlusion data from the camera. It models task execution in terms of the markings of a Petri net. The provided Petri net encodes all possible ways to execute the task in a space-efficient manner. Comprehensive evaluations demonstrate the algorithm's robustness and efficiency in non-deterministic task executions.
The sequence of task steps executed by the human serves as input to the intention prediction module, which predicts the next steps using a neural network with a custom-designed feature space. This feature space encodes spatial information to enable efficient, real-time training for each user interacting with the robot. A comprehensive evaluation compares the accuracy of the module with traditional action prediction approaches using data from user interactions with the system.
Finally, this thesis presents a comprehensive study on the system. Based on intention prediction, different robot behaviours are implemented and evaluated by a user study in terms of fluency and productivity questionnaires. Post-hoc analysis provides insights into the interrelationships and effects of robot behaviour on these measures.
In summary, this thesis contributes technical foundations, empirical evaluations, and motivates further investigations into fluent and flexible human-robot teaming with intention prediction.
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- Software: https://github.com/ubt-ai3/fluentHRC/tree/0.9.0 (URL)
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- https://github.com/ubt-ai3/fluentHRC
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