Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors
Why this work is in the frame
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Bibliographic record
Abstract
Bionic prostheses have restorative potential. However, the complex interplay between intuitive motor control, proprioception, and touch that represents the hallmark of human upper limb function has not been revealed. Here, we show that the neurorobotic fusion of touch, grip kinesthesia, and intuitive motor control promotes levels of behavioral performance that are stratified toward able-bodied function and away from standard-of-care prosthetic users. This was achieved through targeted motor and sensory reinnervation, a closed-loop neural-machine interface, coupled to a noninvasive robotic architecture. Adding touch to motor control improves the ability to reach intended target grasp forces, find target durometers among distractors, and promote prosthetic ownership. Touch, kinesthesia, and motor control restore balanced decision strategies when identifying target durometers and intrinsic visuomotor behaviors that reduce the need to watch the prosthetic hand during object interactions, which frees the eyes to look ahead to the next planned action. The combination of these three modalities also enhances error correction performance. We applied our unified theoretical, functional, and clinical analyses, enabling us to define the relative contributions of the sensory and motor modalities operating simultaneously in this neural-machine interface. This multiperspective framework provides the necessary evidence to show that bionic prostheses attain more human-like function with effective sensory-motor restoration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it