Self-contained measurement of dynamic legged locomotion: design for robot and field environments
Why this work is in the frame
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Bibliographic record
Abstract
Currently there is no robotic solution that surpasses the grace, capabilities, and speed of mammalian legged locomotion. Underlying an ability to analyze and synthesizes this motion is the challenge of efficiently overcoming the discontinuous dynamics, without constraining the agility, of dynamic legged locomotion. Recent strides in biology and robotics have shed new light on the governing principles underlying this motion. The dynamics are central to modeling and driving the legged motion. Consideration of these principles has lead to the research and design of the Kinetically Ordered Locomotion Tetrapod (KOLT) galloping robot. This research recasts galloping in an engineering framework and defines a simple gait classification method based energy phases. This framework is also used to define the gallop using a hybrid model consisting of flight phase, single-contact, and double-contact. This research leverages theory in sensor design and estimation to develop an integrated hybrid estimation method based on the eight-step model. This was tested via laboratory experiments on KOLT and demonstrated in the field using a Labrador retriever. This results of this work show improved motion estimation by combining kinetic state models with inertial measurements and measurement aids (such as: visual or range data). The major contributions of this research program are three-fold: (1) the discovery of new theories/methods to relate different, but related, sensor measurements to gain a more certain state estimate; (2) the integration of these methods into a field robust hardware package and, (3) the demonstration of collection and analysis of biomechanical data in field. The matter of how a robot (or animal) reacts over terrain is deceptively simple, i.e., it thrusts and the rest is governed by Newton's laws of motion. Modeling this in detail however, still remains a significant challenge. By providing a robust estimate of the motion and its dynamics, the methods presented in this thesis move one step closer towards the great promise of fielded dynamic legged locomotion.
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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