A Gecko‐Inspired Robot Using Novel Variable‐Stiffness Adhesive Paw Can Climb on Rough/Smooth Surfaces in Microgravity
Why this work is in the frame
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Bibliographic record
Abstract
Space‐wall‐climbing robots face the challenge of stably attaching to and moving on spacecraft surfaces, which include smooth flat areas and rough intricate surfaces. Although adhesion‐based wall‐climbing robots demonstrate stable climbing on smooth surfaces in outer space, there is scarce research on their stable adhesion on rough surfaces within a microgravity environment. A novel adhesive material is developed inspired by the adhesion mechanism and locomotion of the Gekko gecko. This material exhibits exceptional adhesion across various materials and surface roughness. A variable‐stiffness gecko‐inspired paw is engineered, generating substantial adhesion forces while minimizing detachment forces. Impressively, this paw generates up to 180 N of adhesion force on smooth surfaces and achieves detachment without external forces. By integrating such variable‐stiffness paws with a wall‐climbing robot, a gecko‐inspired robot effectively operating in a microgravity environment is created. The robotic satellite surface climbing experiments and robotic satellite capture experiments are conducted using a simulated microgravity environment and a satellite model. The results unequivocally demonstrate the gecko‐inspired robot's proficiency in executing various functions, including stable motion and capture on both smooth and rough spacecraft surfaces within a microgravity environment. These experiments underscore the potential of adhesion‐based gecko‐inspired robots for in‐orbit services and spacecraft capture and recovery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it