Plenary talks: From whiskers to fingertips — A biomimetic approach to active touch sensing
Bibliographic record
Abstract
How do animals understand the physical world they live in? One answer, due to Gibson, is that their sensory systems are tuned to pick up relevant affordances for behavior, but how is it that the brain and the sensory apparatus become suitably adapted to perform this feat? To cast light on this question we have been investigating active touch sensing in mammals, including humans, and developing biomimetic robots that can help us understand these biological systems whilst also developing useful haptic technologies. An important focus has been on the vibrissal (whisker) system of rodents, and its emergence through evolution and development, which we have investigated through a combination of (i) ethological studies of behaving animals, (ii) computational neuroscience models of the neural circuits involved in vibrissal processing, and (iii) biomimetic robots embodying many of the characteristics of whiskered animals in their design and control. This work has resulted in a series of whiskered robots, the most recent of which, Shrewbot, is able to construct tactile maps of its environment and recognize and track moving objects. We are also studying humanoid touch, focusing on the development of Bayesian strategies for active tactile sensing with robot hands. Here our results have provided the first demonstration of hyperacuity in robot touch whilst also indicating that tactile perception is improved in unstructured environments by appropriate active control. The active sensing framework can also be applied to the development of haptic interfaces for human users that can augment our existing sensory capability. For instance, we are developing a head-mounted “remote touch” system that links distance sensors (ultrasound arrays) with vibrotactile displays. Here an interesting question is how the signals that are delivered through the displays should be modulated to take into account the intentional head and body movements of the user and in order to provide a meaningful and intuitive experience. The talk will present converging lines of evidence, from these different research strands, for the importance of active control in haptics. Our results will also be used to illustrate how experimental, computational, and robotic approaches can operate together to advance our understanding of sensorimotor cognition in behaving systems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.137 | 0.067 |
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".