arXiv:2506.08795v2 Announce Type: replace-cross Abstract: Limb loss affects millions globally, impairing physical function and reducing quality of life. Most traditional surface electromyographic (sEMG) and semi-autonomous methods require users to generate myoelectric signals for each control, imposing physically and mentally taxing demands. This study aims to develop a fully autonomous control system that enables a prosthetic hand to automatically grasp and release objects of various shapes using only a camera attached to the wrist. By placing the hand near an object, the system will automati
Advances in computer vision and imitation learning are enabling more sophisticated and autonomous robotic control, moving beyond traditional bio-signal dependency for prosthetics.
This development significantly lowers the barrier to effective prosthetic use, improving quality of life and potentially expanding the addressable market for advanced prosthetics.
Prosthetic control shifts from requiring taxing bio-signals to autonomous, camera-based object manipulation, making advanced prosthetics more accessible and less burdensome for users.
- · Prosthetics manufacturers
- · Individuals with limb loss
- · Computer vision companies
- · AI hardware developers
- · Manufacturers of sEMG-only prosthetic systems
- · Bio-signal interface developers
Patients with limb loss gain access to more functional and user-friendly prosthetic devices.
The demand for advanced prosthetic R&D, particularly in computer vision and embodied AI, will increase.
This technology could eventually be adapted for broader applications in robotics demanding autonomous object manipulation in unstructured environments.
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Read at arXiv cs.AI