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Adaptive DoF: Concepts to Visualize AI-Generated Movements in Human-Robot Collaboration

EasyChair Preprint no. 8846

3 pagesDate: September 22, 2022


Nowadays, robots collaborate closely with humans in a growing number of areas. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, supporting people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their motion intent and comprehending how they "think" about their actions. We work on solutions that communicate the cobots AI-generated motion intent to a human collaborator. Effective communication enables the user to proceed with the most suitable option. We experiment with different visualization techniques to optimize this user understanding, resulting in increased safety and end-user acceptance.

Keyphrases: cobot, Human-Robot Collaboration, intention feedback, neural network, Visualization techniques

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Max Pascher and Kirill Kronhardt and Til Franzen and Jens Gerken},
  title = {Adaptive DoF: Concepts to Visualize AI-Generated Movements in Human-Robot Collaboration},
  howpublished = {EasyChair Preprint no. 8846},

  year = {EasyChair, 2022}}
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