Recent Advances in Legged Robot Locomotion and Control
Bibliographic record
Abstract
In recent years, legged robots have emerged as powerful mobile robots capable of navigating complex and uncontrolled environments where wheeled robots often fail. This review provides a comprehensive overview of recent studies and advances in legged robot locomotion and control, focusing on integrating fundamental control strategies. The paper begins by examining the evolution of legged robot designs, from early prototypes to quadrupeds and humanoids. It then discusses critical locomotion modeling, including simplified spring-mass systems and full-body dynamic simulations, and highlights how they utilize control strategies. Recent developments in model-based control, such as whole-body and model predictive control, are compared with emerging learning-based methods like reinforcement learning and hybrid control architectures. This paper additionally focuses on addressing the challenges of sensing, terrain adaptation, and the integration of perception for real-time gait adjustment. Finally, the paper identifies ongoing challenges such as energy efficiency, robustness, and sim-to-real transfer, offering perspectives on future research directions. This review aims to serve as a valuable resource for researchers and practitioners seeking to advance the capabilities of legged robotic systems.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".