A comprehensive review of tactile sensing technologies in space robotics
Bibliographic record
Abstract
This review explores the current state and future prospects of tactile sensing technologies in space robotics, addressing the unique challenges posed by harsh space environments such as extreme temperatures, radiation, microgravity, and vacuum conditions, which necessitate specialized sensor designs. We provide a detailed analysis of four primary types of tactile sensors: resistive, capacitive, piezoelectric, and optical, evaluating their operating principles, advantages, limitations, and specific applications in space exploration. Recent advancements in materials science, including the development of radiation-hardened components and flexible sensor materials, are discussed alongside innovations in sensor design and integration techniques that enhance performance and durability under space conditions. Through case studies of various space robotic systems, such as Mars rovers, robotic arms like Canadarm, humanoid robots like Robonaut, and specialized robots like Astrobee and LEMUR 3, this review highlights the crucial role of tactile sensing in enabling precise manipulation, environmental interaction, and autonomous operations in space. Moreover, it synthesizes current research and applications to underscore the transformative impact of tactile sensing technologies on space robotics and highlights their pivotal role in expanding human presence and scientific understanding in space, offering strategic insights and recommendations to guide future research and development in this critical field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".