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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Human-Robot Interaction
  • Design Aesthetics

Background:

  • Generative Artificial Intelligence (AI) tools have become prevalent in design.
  • Soft robotics offers unique affordances for human interaction through biomorphic qualities.
  • Soft Biomorphism is an aesthetic paradigm focused on these inherent biomorphic features.

Purpose of the Study:

  • To explore soft robotics designs using generative AI.
  • To investigate how AI can expand the design space of soft biomorphic robots.
  • To uncover novel interpretations and hybrid designs through AI image generation.

Main Methods:

  • Utilized generative AI, specifically text-to-image (TTI) and image-to-image (ITI) generation techniques.
  • Conducted two experiments to explore AI's capability in articulating soft biomorphism.
  • Experiment 1: TTI generation for alternative biomorphic interpretations.
  • Experiment 2: Combined TTI and ITI generation for hybrid designs.

Main Results:

  • Experiment 1 revealed novel material aesthetics, such as incorporating fur, expanding soft biomorphism interpretations.
  • Experiment 2 discovered hybrid techno-organic robot designs merging rigid and pliable materials.
  • AI image generation effectively articulated and expanded the design possibilities for soft biomorphic robots.

Conclusions:

  • Generative AI significantly contributes to expanding the design space of soft robotics.
  • AI facilitates the discovery of novel aesthetic dimensions and hybrid material compositions in soft robots.
  • This approach demonstrates practical applications of AI in advancing soft robotics design.