Nature redux: interrogating biomorphism and soft robot aesthetics through generative AI

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.