Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

<p>Part I: Tactile sensing and perception<br>1. Tactile sensors for dexterous manipulation<br>2. Robotic perception of object properties using tactile sensing<br>3. Multimodal perception for dexterous manipulation<br>4. Using Machine Learning for Material Detection with Capacitive Proximity Sensors</p> <p>Part II: Skill representation and learning<br>5. Admittance control: learning from human and collaboration with human<br>6. Sensorimotor Control for Dexterous Grasping--Inspiration from human hand<br>7. Efficient Haptic Learning and Interaction<br>8. From human to robot grasping: kinematics and forces synergies<br>9. Learning a form-closure grasping with attractive region in environment<br>10. Learning hierarchical control for robust in-hand manipulation<br>11. Learning Industrial Assembly by Guided-DDPG</p> <p>Part III: Robotic hand adaptive control<br>12. The novel poly-articulated prosthetic hand Hannes: A survey study, and clinical evaluation<br>13. Enhancing vision control by tactile sensing for robotic manipulation<br>14. Neural Network enhanced Optimal Control of Manipulator<br>15. Towards Dexterous In-Hand Manipulation of Unknown Objects: A Feedback Based Control Approach<br>16. Learning Industrial Assembly by Guided-DDPG</p>