Multimodal Sensory Integration and Control for Interactive Dexterous In-Hand Object Manipulation

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Dexterous manipulation skills are one of the most remarkable features of human behavior. Even seemingly simple tasks of manipulating tools or objects embody feats of enormous complexity to process various sensory inputs and to make necessary adjustments all the while retrieving and updating motion vocabularies encoded in our brain. Traditionally, motion control for robot manipulators has pursued high-speed and high-accuracy trajectory tracking rather than natural, adaptable and dexterous movements. They enjoyed major success in factory environments (e.g. welding robots in automotive factories) but do not adequately apply to unstructured and dynamic environments that require interaction with random ’every-day’ objects or with humans. This proposal will attempt to tackle these issues by reaching beyond conventional methods to leverage sensor technologies, exploit intelligent decision making and control theories, and carefully observe how human hand and arm movements are coordinated during critical stages of object manipulation tasks. This research will take a bio-inspired engineering approach in that we will employ advanced sensing techniques to investigate how high-fidelity tactile data are integrated with proprioceptive (position, force/torque) and visual data to achieve ‘dexterity’ during object manipulation tasks, e.g. how finger-tip forces are regulated and how the hand and arm muscles are coordinated. The technical objectives of this research include 1) to develop integrated multimodal state estimator for simultaneous estimation of the robot, the object and the environment, 2) to investigate stereotyped patterns of tactile and proprioceptive sensory responses of humans during interactive object manipulation tasks, 3) to apply this knowledge to developing novel reflex (low-level, feedback) and high-level (motion planning, force/torque) controllers to enhance the sophistication of controlled motion of a robotic arm and hand during interaction periods, and 4) to develop learning control policies that enable the completion of the full cycle of manipulation phases at faster speed and with smoother transitions. Understanding human sensorimotor control and synthesizing artificial systems of similar capabilities will profoundly impact a wide range of applications including agile manufacturing, home automation, medical robots, prosthetics, rehabilitation engineering, and technologies that support the public welfare for aging population. Furthermore, the scientific findings and engineering achievements pursued through this research will place Canada at the forefront of research in key areas of intelligent control, smart sensory data processing, next generation of robotic manipulation and bio-mechatronics, which will also inspire and train the next generation of highly qualified talent who will contribute directly to innovation in these key sectors.