SHIFT: An Interdisciplinary Framework for Scaffolding Human Attention and Understanding in Explanatory Tasks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| author | Groß, André Richter, Birte Wrede, Britta |
| author_facet | Groß, André Richter, Birte Wrede, Britta |
| contents | In this work, we present a domain-independent approach for adaptive scaffolding in robotic explanation generation to guide tasks in human-robot interaction. We present a method for incorporating interdisciplinary research results into a computational model as a pre-configured scoring system implemented in a framework called SHIFT. This involves outlining a procedure for integrating concepts from disciplines outside traditional computer science into a robotics computational framework. Our approach allows us to model the human cognitive state into six observable states within the human partner model. To study the pre-configuration of the system, we implement a reinforcement learning approach on top of our model. This approach allows adaptation to individuals who deviate from the configuration of the scoring system. Therefore, in our proof-of-concept evaluation, the model's adaptability on four different user types shows that the models' adaptation performs better, i.e., recouped faster after exploration and has a higher accumulated reward with our pre-configured scoring system than without it. We discuss further strategies of speeding up the learning phase to enable a realistic adaptation behavior to real users. The system is accessible through docker and supports querying via ROS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SHIFT: An Interdisciplinary Framework for Scaffolding Human Attention and Understanding in Explanatory Tasks Groß, André Richter, Birte Wrede, Britta Human-Computer Interaction Robotics In this work, we present a domain-independent approach for adaptive scaffolding in robotic explanation generation to guide tasks in human-robot interaction. We present a method for incorporating interdisciplinary research results into a computational model as a pre-configured scoring system implemented in a framework called SHIFT. This involves outlining a procedure for integrating concepts from disciplines outside traditional computer science into a robotics computational framework. Our approach allows us to model the human cognitive state into six observable states within the human partner model. To study the pre-configuration of the system, we implement a reinforcement learning approach on top of our model. This approach allows adaptation to individuals who deviate from the configuration of the scoring system. Therefore, in our proof-of-concept evaluation, the model's adaptability on four different user types shows that the models' adaptation performs better, i.e., recouped faster after exploration and has a higher accumulated reward with our pre-configured scoring system than without it. We discuss further strategies of speeding up the learning phase to enable a realistic adaptation behavior to real users. The system is accessible through docker and supports querying via ROS. |
| title | SHIFT: An Interdisciplinary Framework for Scaffolding Human Attention and Understanding in Explanatory Tasks |
| topic | Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2503.16447 |