Featured paper: Leveraging LSTM, tactile sensors, and haptic feedback to augment prosthetic control via grasp type prediction and grasp type feedback

In this episode, learn how AI and soft touch sensors are helping prosthetic hands feel more natural by translating grip type into real-time haptic feedback. Discover how tactile “skin,” LSTM pattern recognition, and vibration cues can give users better control, faster object handling, and a stronger sense of embodiment.
*Disclaimer: This content was generated by NotebookLM and has been reviewed for accuracy by Dr. Tram.*

Featured paper: Neural sampling from cognitive maps enables goal-directed imagination and planning

*Disclaimer: This content was generated by NotebookLM and has been reviewed for accuracy by Dr. Tram.*

Featured paper: On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning

What if your prosthetic hand could think for itself? In this episode, discover how artificial intelligence and computer vision are teaching prosthetic hands to recognize objects and grip them autonomously. Learn about reinforcement learning algorithms, the power of visual feedback, and why some objects—like a bar of soap—still challenge our smartest robots. Plus, find out how this technology could cut training time from months to minutes and transform prosthetics from tools you control to partners that work with you.*Disclaimer: This content was generated by NotebookLM and has been reviewed for accuracy by Dr. Tram.*