Bibliographic record
Abstract
1. INTRODUCTIONAs a result of more than millions of years' evolution, legged animals have showcased unrivaled sophisticated mobility, maneuverability and adaptation to complex environments, and have continuously inspired legged robot design. Bio-inspired legged robots have unique potential advantages in applications which require the transversal on rough terrain, exploration of unstructured environments and navigation in complex environments with obstacles that call for advanced maneuverability and control. In addition to the exceptional mobility, legged animals have established a complex sensing system with the environment that is significantly advantageous over the state-of-the-art robot sensation. With the impressive progress of artificial intelligence and sensing technologies in recent years, the bio-inspired legged robots have also experienced notable growth, accompanied by tremendous challenges though. This special collection aims to disseminate some of the latest advancements in the design, sensing and control of bioinspired legged robots.2. OVERVIEW OF THE PAPERS IN THIS SPECIAL ISSUEThe selected papers include theoretical and experimental research work on bicycleinspired balance control method for quadruped robots [1]; advancements of the understanding of the neural activity of robust robot locomotion control from computational neuroscience within deep reinforcement learning [2]; system design of a pneumatic-driven musculoskeletal bipedal robot with its sequential jumping experimental validation [3]; investigations on the postural stabilization for a musculoskeletal robot during rapid and powerful hopping actions through the emulation of biarticular thigh muscles activation, along with experimental validation [4]; and the exploration of unknown environments using generalized autonomous mobile robots within simultaneous learning of the environments and robot transversality [5].3. CONCLUSIONIn conclusion, this special issue covers a broad range in the design, sensing and control of bio-inspired legged robots, and represents a significant step forward in the latest bioinspired legged robot research. All papers in this issue present original, inspirational ideas, with a clear indication of problem formulation and methodologies, convincing experimental validations, potential applications, and proper paper organization. The achievements presented in these papers address some of the challenges and advance research on bio-inspired legged robot design, sensing and control. We hope this Special Issue will be helpful to enhance understanding and further research in this exciting field, and promote academic and industrial attention.4. ACKNOWLEDGEMENTWe thank all authors and all guest associate editors, review editors, and peer reviewers for their valuable contributions to the Special Issue ‘Bio-Inspired Legged Robotics: Design, Sensing and Control’.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".