Hierarchical Learning and Control for In-Hand Micromanipulation Using Multiple Laser-Driven Micro-Tools

Abstract

Laser-driven micro-tools are formulated by treating highly-focused laser beams as actuators, to control the tool's motion to contact then manipulate a micro object, which allows it to manipulate opaque micro objects, or large cells without causing photodamage. However, most existing laser-driven tools are limited to relatively simple tasks, such as moving and caging, and cannot carry out in-hand dexterous tasks. This is mainly because in-hand manipulation involves continuously coordinating multiple laser beams, micro-tools, and the object itself, which has high degrees of freedom (DoF) and poses up challenge for planner and controller design. This paper presents a new hierarchical formulation for the grasping and manipulation of micro objects using multiple laser-driven micro-tools. In hardware, multiple laser-driven tools are assembled to act as a robotic hand to carry out in-hand tasks (e.g., rotating); in software, a hierarchical scheme is developed to shrunken the action space and coordinate the motion of multiple tools, subject to both the parametric uncertainty in the tool and the unknown dynamic model of the object. Such a formulation provides potential for achieving robotic in-hand manipulation at a micro scale. The performance of the proposed system is validated in simulation studies under different scenarios.