Integrated Machine Learning and Control for Synthesis of Dexterous Manipulation Skills

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The rise of collaborative robots promises a new era that robots can coexist with humans in our daily lives, be it home or workplace. Recent strides in machine learning have quickly transformed the way we design autonomous systems even under conditions previously considered too complex to replace humans (e.g. driving a car or playing the game of go). While these data-driven tools offer promises in knowledge-based reasoning such as analyzing image or audio data, it is still a question how such techniques can be transferred to the complex motor control of mechanical devices such as robotic arm and hand that are governed by principles of mechanics. Human extremities are vastly superior to artificial counterparts; this is not in the power and accuracy in executing the pre-programmed task repeatedly, but rather to the richness, adaptability and the sophistication of skilled movements for complex and unpredictable tasks. While we do not yet understand the mechanisms of human sensorimotor coordination, we agree undeniably that human motor skills are mastered through years of practice and learning. The PI has established a leading program focused on the application of advanced sensory data processing and model-based control approaches to enhancing the functionalities of mechanical manipulators. Building on this expertise, the main goal of the proposed research program is to combine recent AI (artificial intelligence) and machine learning techniques with traditional model-based control strategies to endow robotic arms and hands with human-like manipulation skills and adaptability. Specifically, this research aims 1) to develop multi-modal training strategies for semantic perception of various objects and features associated with manipulation tasks, 2) to investigate inverse reinforcement learning (RL) strategies to understand mechanisms behind goal-directed manipulation tasks by human experts, 3) to integrate a priori knowledge on dynamic models of the robot and the environment to enhance the performance of RL, and 4) to develop new simulated training environments as well as a hierarchical RL structure for efficient learning. Successful application of machine learning to physical systems holds enormous potential for breakthroughs in the way we design and operate mechatronic systems by allowing them to seamlessly operate within complex and uncertain environments while constantly interacting with humans and unknown objects. The practical value of the new knowledge and fundamental technologies to be generated through this research program will have a substantial impact on a wide range of industrial and public sectors including Canada's manufacturing, logistics, supply chain, and health care industries. Research tasks associated with this program cut across different disciplines of mechanics, robotics, control theory, signal processing, AI and machine learning, thereby providing ideal training environments to our next generation of highly qualified personnel.