The deep learning model has significantly advanced dexterous manipulation techniques for multi-fingered hand grasping, but contact information-guided grasping in cluttered environments remains largely underexplored.
Today's robots are very much built upon natural inspirations; however, the limitations of biological designs can confine the potential of robotic hands.
Designing a human-inspired robotic hand that can bend backward and detach itself taps into the uncanny valley, suggesting a way forward for diverse object manipulation.
The research showcases what robotics can achieve when breaking free from the constraints of nature, leading to innovative approaches in dexterous manipulation.
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