Precise and dexterous manipulation of micro-objects with the size of a few microns is significant but challenging. For example, peeling the inner limiting membrane with a thickness of 2.5 microns during vitreoretinal surgery or transporting red blood cells with the size of 5-8 microns is quite challenging, since the human hand can only reach a precision of 100 microns for operation under optimal conditions. Therefore, a robotic micromanipulation system is worth developing, which can support fields of application from biotechnology to microsurgery.

This project is motivated by the urgent need for intuitive and flexible micromanipulation systems to support manipulation of micro-object, where the poor sensory feedback, physiological tremor, and obstructive view hamper the precise micron-scale maneuvers. To this end, I aim to develop adaptive human-robot shared control for micromanipulation.

In conventional robotized automation, robots are programmed to execute repetitive tasks with high speed and high precision. However, it is impractical to hard-code robots to perform dexterous manipulation in various scenarios, since it involves dynamic processes that are difficult to model.

In recent years, service robots have been widely deployed in different environments such as houses, offices, hospitals.  They have been developed to perform a wide range of human activities such as cooking, cleaning, sweeping, personal care etc. Among the diverse activities mentioned above, pouring a specific amount of liquids or solid materials such as rice, sugars and salts into a container is one of the frequently performed tasks in food preparation, drink service or industrial environments. The pouring system is a complicated nonlinear time-variant system. An accurate model of liquid dynamics is difficult to obtain. Therefore, in this project, robot learning techniques will be developed for robot to master automatic pouring skills.  

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