The aim of this workshop is to understand how human acquires dexterity in object manipulation, discuss the possibility of its application in robotic systems, and to draw key strategies for dealing with robotic dexterous manipulation in next generation.

The level of dexterous manipulation by robots is currently far from that of human being. What can improve the ability of robots? One hint might be to understand the approaches of human being in dexterity acquisition.

Software viewpoints: Robotic surgical systems are now popular for minimally invasive surgeries. In order to operate the robot, surgeons have to learn complex procedures. The methodology for facilitating the learning process and investigation of human behavior in the learning process could provide an insight for constructing learning based dexterous manipulation by robots. Prosthetic hand and arm are controlled by amputee people via neural signals such as EMG or EEG. The object manipulation has to be done by the limited number of control inputs. The control strategies for prosthetic hand could be a candidate of new control schema for robotic manipulation. The hypothesis of muscle synergy, which is considered to be a key for dexterity in human, also could provide valuable insights on robotic manipulation.

Hardware viewpoints: The structure of human finger and hand play an important role for dexterous manipulation. It was acquired in the process of evolution. The key structures for dexterity are also valid for key structure of robotic hand design.

Software viewpoints can provide new planning strategy or new control schema, while hardware viewpoints can provide new design of robotic hands (and arms). By elevating a relationship between the both viewpoints, this workshop will accelerate to generate next challenge topics in dexterous manipulation by robots.

After introducing these topics in oral, poster session will be held for making an opportunity of close interaction between researchers in different fields, in order to make a trigger for next object manipulation challenge.