Intelligent Robotics Across Scales
Research on intelligent robotics lies at the intersection of machine learning and robotics.
The ultimate goal is to develop the next-generation robots empowered by artificial intelligence with super-human capabilities. We envision that intelligent robots will reshape our world by providing tangible benefits in our daily life, contributing to the healthcare system or assisting humans in hazardous environments.
I proposed the concept of implicit human-robot shared control, which means that the human operator can conduct operations with the robot through an intelligent interface. The key components of master-slave mapping are explored, while an adaptive mechanism is incorporated into the control framework to improve the efficiency of teleoperation. Context-awareness and human intention recognition are explored to implement an adaptive motion scaling framework, which enhances surgical operation efficiency. A hybrid interface for robot control is proposed to enable the combination of the advantages of different mapping strategies. For dexterous micromanipulation at the cellular level, microrobots with complex shape for the implementation of out-of-plane control is investigated, which can serve as a dexterous tool for indirect micro/nano-scale object manipulation. Machine learning-based vision tracking techniques for depth estimation and pose estimation are developed for microrobot monitoring during optical manipulation. Two control strategies for distributed force control of optical microrobots were developed and verified.
Enable seamlessly human-robot shared control
Enhance the level of autonomy for multi-scale robotics systems
My major research interests lie at the intersection of robotics and machine learning. I envision that intelligent robots will reshape our world by providing tangible benefits in our daily life, contributing to the healthcare system, or assisting humans in hazardous environments. In addition, I also work on micro-robotics, teleoperation, and human-robot shared control.
Medical robots and instruments are expected to be smaller, and smarter for wider clinical uptake. Intelligent robotic platforms and accurate micromanipulation techniques are worth developing since they can empower operators with superhuman capabilities as well as reduce their physical workload during repetitive and tedious micro-surgical tasks. Despite continuous technical evolution in this research area, many challenges remain. For micromanipulation platforms that can conduct surgery in the microscale, it’s challenging to enable precise perception of the micro-tools and dexterous manipulation in 3D space. Therefore, new control strategies and vision techniques are worth exploring. To develop smarter robotic platforms, machine learning techniques can be incorporated into the control scheme. Therefore, it’s worthwhile to combine control commands generated by human operators and machine learning-based autonomous control to enable efficient human-robot shared control for surgical operations. This leads to the development of intelligent surgical robots to assist human operators in robotic surgery.
Dandan Zhang* (first author), et al, “Micro/Nano-Objects Pose Estimation Via a Sim-to-Real Learning-to-Match Approach Using Small Dataset”,
Communication Physics-Nature Portfolio (accepted in early 2022).
Dandan Zhang (first author), et al, “Explainable Hierarchical Imitation Learning for Robotic Drink Pouring”,
IEEE Transaction on Automation Science and Engineering, 2021.
“Progress in robotics for combating infectious diseases”,
Science Robotics, 2021.
Department of Engineering Mathematics
University of Bristol
Ada Lovelace Building,
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