Research Areas
Real Time Robotic Motion Planning
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Hardware accelerated real-time robot motion planning on a configurable parallel computing chip
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Community-based open source development platforms for extremely fast robot optimization and simulation
Intelligent and Adaptive Feedback Control Theory for Robotics
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Multivariate adaptive feedback control policy design using deep reinforcement learning
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Adaptive optimal control learning by computationally efficient trajectory optimization
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Stochastic verification of the robustness and stability of learning-based control policies
Assistive Robotic Technology for Medical Assistive and Rehabilitation Devices
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Enhanced mobility for the elderly and people with paraplegia using a powered lower-limb exoskeleton
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Clinical study of a powered lower-limb exoskeleton for people with paraplegia: energy efficiency, user comfort, augmented rehabilitation, and safety
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Decentralized feedback control design for interactive human-exoskeleton systems
Advanced Legged Locomotion
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Networked control design for coordinated locomotion and manipulation of home assistant humanoids
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Autonomous visual navigation of legged robots in the complex real-world environment

My research interests lie at the intersection of nonlinear control and optimization theory, with a particular focus on developing novel control solutions for complex robotic systems. The central objective of my research is to develop computationally tractable optimization-based control methodologies that unify formal control theory with advanced numerical optimization techniques and, ultimately, to realize versatile and dynamic maneuvers experimentally on a variety of robotic platforms including home assistant robots and robotic exoskeletons.