Research

My research centers on the optimal control and design of Mechanically Adaptive Robots—machines that adapt and improve through repeated experiences, much like humans. This includes developing control algorithms for real-time optimal control and designing actuators that make mechanical adaptation possible. The work focuses on the theory and creation of devices such as actuators, robotic limbs, and exoskeletons to advance robot autonomy and enhance human mobility. This multidisciplinary research has been supported by the Singaporean Ministry of Education and the National Science Foundation, earning recognition through two of the field’s highest honors: the IEEE Transactions on Robotics Best Paper Award and the National Science Foundation CAREER Award.

Projects

Mechanically Adaptive Robot Exoskeletons to Improve Human Mobility

Mechanically Adaptive Robot Exoskeletons to Improve Human Mobility

We work on the theory of mechanically adaptive robot exoskeletons, to create unpowered but mechanically-adaptive devices and devices driven by small actuators (for example, a small electric motor), that provide the human with capabilities beyond those of an unassisted human, to an extent previously thought to require large externally powered actuators (for example, large electric […]
Mechanically Adaptive Actuators to Advance Robot Autonomy

Mechanically Adaptive Actuators to Advance Robot Autonomy

Actuators are one of the key components of robots; they are used in industrial robots, humanoids, but also automobiles. We focus on developing compliant actuators which consist of springs attached in series or parallel with motors. The most common types of compliant actuators are series elastic actuators (SEAs) and variable stiffness actuators (VSAs). We invent […]
”Robots Teaching Robots” as a Real-time Optimal Control Paradigm

”Robots Teaching Robots” as a Real-time Optimal Control Paradigm

We promote the optimization of robot performance via real-time experiments guided by dedicated teacher robots, instead of optimizing system performance guided by uncertain model-based predictions and measured data. The method we develop is where robots are used to develop controllers for robots in a somewhat analogous way as parents teach their children. The technique may […]
Reverse-Engineering Nature for Locomotion Control in Robots

Reverse-Engineering Nature for Locomotion Control in Robots

Understanding the principles underlying human motion is the first step in developing robots for human assistance and augmentation; exoskeletons for rescue operations, or to assist in case of locomotor impairment due to illness or injury. In this research, we investigate approximations of the complex biological locomotion controller that provides simultaneous limb coordination and body stabilization […]
Physics-based Numerical Simulation Algorithms for Predicting Robot Behavior

Physics-based Numerical Simulation Algorithms for Predicting Robot Behavior

Robots that interact with the environment are constrained dynamical systems. Constrained dynamical systems can be modeled using a set of differential equations subject to holonomic and non-holonomic constraints; algebraic constraints. One way to approximately model the constrained dynamics of these robots, for example walking robots, is to use switching-differential-algebraic equations (SDAEs), defined by a set […]