Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract By virtue of distinguished wing shape morphing characteristics, the unrivaled agility and flight maneuverability of bats have inspired scientists and engineers to develop novel forms of robots that can fly like bats. The unique wing conformations, flight kinematics, and aerodynamics offer significant advantages over the conventional form of miniature air vehicle in terms of quiet, safe operations, improved efficiency, and enhanced maneuverability. Meanwhile, they also pose substantial challenges for robot design from multiple perspectives, including mechanical design, sensing, control, etc. The practical benefits and technical bottleneck have motivated the development of bat-inspired robots in recent years. The purpose of this paper is to summarize the designing principles and report current state-of-the-art of bat-inspired robot designs, emphasizing the respective distinguishing features of each paradigm, along with the room for further improvement. Rather than showcasing advancement in wing materials, we will focus on the mechanical design and control methodology. This paper will help researchers new in this realm to get familiar with the bat-inspired robots by adopting features from existing designs. It also concludes technical challenges associated with future development, involving biological research, aerodynamic modeling, mechanical design, and control technique.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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