A Unified Foot–Terrain Interaction Model for Legged Robots Contacting With Diverse Terrains
Bibliographic record
Abstract
Interaction with the terrain is essential for legged robots to adapt to complex environment. Field terrain could be steep, slippery, and muddy. Legged robots may sink on sand or mud and slip on ice or snow. To address such problems, foot–terrain interaction models have been developed to preestimate the sink or slip state of the robot and models are switched to adapt to different terrain. However, it is difficult to switch the model precisely, which causes problems in its application to complex terrain in the field. This article proposes a unified foot–terrain interaction model to avoid model switching in multiphysical characteristic terrains. Specifically, a normal foot–terrain interaction model is formulated to characterize the dynamic sinkage of robot foot into soft terrain which combines velocity and loading effects on the sinkage exponent. Furthermore, a sinkage-slip model is proposed to reflect sliding friction and bulldozing resistance. Finally, by generalizing the models between different terrains, a unified model of hard–soft-slippery terrain is completed in both normal and tangential directions. Single-foot and robot-movement experimental results show that the proposed model can adapt to different field terrains with high accuracy and efficiency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".