Haptic Feedback in Teleoperation in Micro- and Nanoworlds
Bibliographic record
Abstract
Robotic systems have been developed to handle very small objects, but their use remains complex and necessitates long-duration training. Simulators, such as molecular simulators, can provide access to large amounts of raw data, but only highly trained users can interpret the results of such systems. Haptic feedback in teleoperation, which provides force feedback to an operator, appears to be a promising solution for interaction with such systems, as it allows intuitiveness and flexibility. However, several issues arise while implementing teleoperation schemes at the micro- and nanoscale, owing to complex force fields that must be transmitted to users and scaling differences between the haptic device and the manipulated objects. Major advances in such technology have been made in recent years. In this chapter, we review the main systems in this area and highlight how some fundamental issues in teleoperation for micro- and nanoscale applications have been addressed. We consider three types of teleoperation, including (a) direct (manipulation of real objects), (b) virtual (use of simulators), and (c) augmented (combining real robotic systems and simulators). Remaining issues that must be addressed for further advances in teleoperation for micro- and nanoworlds are also discussed, including (a) comprehension of phenomena that dictate very small object (<500 micrometers) behavior and (b) design of intuitive 3-D manipulation systems. Design guidelines to realize an intuitive haptic feedback teleoperation system at the micro- and nanoscale level are proposed.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".