On Slip Detection for Quadruped Robots
Bibliographic record
Abstract
Legged robots are meant to autonomously navigate unstructured environments for applications like search and rescue, inspection, or maintenance. In autonomous navigation, a close relationship between locomotion and perception is crucial; the robot has to perceive the environment and detect any change in order to autonomously make decisions based on what it perceived. One main challenge in autonomous navigation for legged robots is locomotion over unstructured terrains. In particular, when the ground is slippery, common control techniques and state estimation algorithms may not be effective, because the ground is commonly assumed to be non-slippery. This paper addresses the problem of slip detection, a first fundamental step to implement appropriate control strategies and perform dynamic whole-body locomotion. We propose a slip detection approach, which is independent of the gait type and the estimation of the position and velocity of the robot in an inertial frame, that is usually prone to drift problems. To the best of our knowledge, this is the first approach of a quadruped robot slip detector that can detect more than one foot slippage at the same time, relying on the estimation of measurements expressed in a non-inertial frame. We validate the approach on the 90 kg Hydraulically actuated Quadruped robot (HyQ) from the Istituto Italiano di Tecnologia (IIT), and we compare it against a state-of-the-art slip detection algorithm.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".