Bibliographic record
Abstract
Human existence is deeply enmeshed with the natural environment, and humans possess an innate connection with living beings and natural elements. Within art, design, and architecture, this is reflected in the transhistorical concept of biomorphism, which refers to a preference for or an interest in organic, lifelike forms that are evocative of nature and natural organisms.<br/><br/>This zoom.able disseminates research towards envisioning soft biomorphism as an alternative design paradigm for soft robotics (robots made from pliable and elastic materials). Earlier work on soft robotics has mainly been anchored in technical science and focused on improving the abilities of robots through mimicking the physiology and mechanical operations of soft natural organisms. Soft biomorphism seeks to enact a different perspective, to facilitate a reorientation of the field’s interests.<br/><br/>The notion of soft biomorphism is based on the simple premise of interrogating what happens when the inherently organic aesthetic of soft robots is emphasized and enhanced by incorporating inspiration from forms, colors, textures, and patterns of a biological origin. Soft biomorphism differs from other bioinspired design approaches used in robotics, as it eschews exact replication of a particular organism’s morphological features, instead favoring select idiosyncratic or general visual and haptic similarities with natural organisms. Rather than seeking to deceive users into believing that a robot is alive, biomorphic elements are incorporated to cultivate connection and empathy between humans and machines. By appearing lifelike, yet unfamiliar, soft biomorphic robot designs could facilitate more open-ended, negotiable human–robot relations that are not modeled on human–human interaction or interactions with specific animals or domesticated pets. The artistic motivations underlying soft biomorphism also include critically reflecting on the boundaries between nature, technology, and their cultural uses and meanings.<br/><br/>This practice-based work unites approaches from our diverse disciplinary backgrounds within soft robotics, mechanical engineering, human–robot interaction, artistic practice, and design research. We initially sought to unpack and enact soft biomorphism through the construction of a series of material prototypes and actuated behavioral objects. The interaction potentials of these soft biomorphic prototypes were subsequently interrogated in a physical human–robot interaction study (Christiansen et al., 2023).<br/><br/>This zoom.able focuses on articulating soft materiality and the biomorphological character of the prototypes as grounds for sensory perception, a potentiality of bodily sensations, and distinct types of embodied knowledge. While movement, sensing, and artificial cognition are integral to robotics technology and its aesthetics in general, our work investigates how soft materials themselves hold the potential to generate their own forms and relations when embedded in robotics. We further explore the aesthetics of soft biomorphic robots by employing photographs of our physical prototypes and text descriptions of their biomorphic inspirations as inputs for an AI image generation software. The resulting outputs, presented jointly on the final layer of the zoomable, suggest that connections in the web of life self-reflexively query the reification and remediation of biomorphic qualities within the virtual, latent space of global visual culture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".