Binary robotics: robots using arrays of small soft cellular actuators
Bibliographic record
Abstract
Traditional robotics is a well-developed, mature technology. Industrial robots are faster, stronger and more accurate than a human could dream of achieving, but we still outclass them in terms of mobility, versatility and dexterity of manipulation. Classical robots are used mainly to position objects in space very quickly (painting, welding, etc). However, they remain limited to such tasks despite much research and development effort in recent years. This technology is very mature but also close to its limits. A radical change is needed to open new opportunities and to bring robots to a higher level. The development of new actuators will play a big role in this endeavor. CAMUS research group (Université de Sherbrooke, Québec, Canada), explores and develops a new paradigm in robotics that would have several advantages over traditional approaches. The idea is to replace the complex components (seals, bearings, gears, motors, etc.) with a flexible structure including many active elements (artificial muscles). Emerging technologies of actuators, such as shape memory alloys, electro-active polymers, pneumatic muscles and piezoelectric components, make such possibilities more and more achievable. Many of these technologies have the potential to rival the performance of biological muscles and, thereby, to revolutionise robotics. This talk will briefly present the Université de Sherbrooke and current research projects in the CAMUS laboratory. It will then focus on binary robotics. I will present an overview of research carried out in this field, CAMUS’s robotics paradigm using embedded air muscles, and a prototype for a medical application. The talk will end with a discussion of control strategies for such robots.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".