Bioinspired Tactile Object Identification Leveraging Deep Learning and Soft Body Compliance
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".