Using Gait Change for Terrain Sensing by Robots
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
In this paper we examine the interplay between terrain classification accuracy and gait in a walking robot, and show how changes in walking speed can be used for terrain-dependent walk optimizations, as well as to enhance terrain identification. The details of a walking gait have a great influence on the performance of locomotive systems and their interaction with the terrain. Most legged robots can benefit from adapting their gait (and specifically walk speed) to the particular terrain on which they are walking. To achieve this, the agent should first be capable of identifying the terrain in order to choose the optimal speed. In this work we are interested in analyzing the performance of a legged robot on different terrains and with different gait parameters. We also discuss the effects of gait parameters, such as speed, on the terrain identification computed by a legged robot. We use an unsupervised classification algorithm to classify terrains based on inertial measurement samples and actuator feedback collected over different terrains and operation speeds. We present the effects of speed on the terrain classification in our classification results.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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 it