High Performance Differential Elastic Actuator for Robotic Interaction Tasks.
Bibliographic record
Abstract
Abstract- For complex robotic tasks (manipulation, locomotion, haptics,...), the lack of knowledge of precise interaction models, the difficulties to precisely measure the task associated physical quantities (force, speed,...) in real time and the non-collocation of sensors and transducers have negative effects on performance and stability of robots when using simple force or simple movement controllers. To cope with these issues, some researchers proposed a new approach named «interaction control » that refers to regulation of the robot’s dynamic behavior at its ports of interaction with the environment. Interaction control involves specifying a dynamic relationship between motion and force, and implementing a control law that attempts to minimize deviation from this relationship [1]. The implementation of machines able to precisely control interaction with its environment begins with the use of actuators specially designed for that purpose. To that effect, a new compact implementation design for high performance actuators that are especially adapted for integration in robotic mechanisms is presented, this design making use of a mechanical differential as central element. Differential coupling between an intrinsically high impedance transducer and an intrinsically low impedance spring element provides the same benefits as serial coupling [4]. However differential coupling allows new interesting design implementations possibilities, especially for rotational actuators I.
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.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".