Vision-Based Reactive Temporal Logic Motion Planning for Quadruped Robots in Unstructured Dynamic Environments
Bibliographic record
Abstract
Temporal logic-based motion planning has been extensively studied to address complex robotic tasks. However, existing works primarily focus on static environments or assume the robot has full observations of the environment. This limits their practical applications since real-world environments are often dynamic, and robots may suffer from partial observations. To tackle these issues, this study proposes a framework for vision-based reactive temporal logic motion planning (V-RTLMP) for robots integrated with LiDAR sensing. The V-RTLMP is designed to perform high-level linear temporal logic (LTL) tasks in unstructured dynamic environments. The framework comprises two modules: offline preplanning and online reactive planning. Given LTL specifications, the preplanning phase generates a reference trajectory over the continuous workspace via sampling-based methods using prior environmental knowledge. The online reactive module dynamically adjusts the robot trajectory based on real-time visual perception to adapt to environmental changes. Extensive numerical simulations and real-world experiments using a quadruped robot demonstrate the effectiveness of the proposed vision-based reactive motion planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".