Bioinspired Tactile Object Identification Leveraging Deep Learning and Soft Body Compliance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tactile object identification is a fundamental human skill, underlying several core aspects of human intelligence. Humans display a range of remarkable haptic skills, enabled by the synergistic interactions of the somatosensory system with higher‐level cognitive processes. In contrast, robotics’ haptic sensing solutions have historically lacked the ability to achieve human‐level perceptive capabilities, lacking in both the sensory system and its cognitive digital counterpart. Herein, part of this challenge is addressed by leveraging the success of the fields of soft robotics and deep learning to show how a soft robotic hand, equipped with low‐resolution tactile sensing, can be used to accurately identify a diverse set of objects. In particular, ROSE‐Net, a neural network that leverages multiple grasps to enable accurate pose‐invariant object recognition, is developed. The multi‐grasp haptic discrimination solution can lead to a significant increase in performance. The versatility and adaptability of this approach are also tested in two scenarios: a learning transfer scenario and a fault tolerance scenario. Finally, the framework is tested in an online discrimination task, where this approach is shown to naturally require additional grasps for objects that are more challenging to identify using a single grasp and low spatial resolution tactile sensing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it