David Braun Email & Phone Number

Nashville, Tn, Us

Nashville, Tn, Us

United States

• Pioneered research on mechanically adaptive robots, including the development of energetically-conservative variable stiffness mechanisms, supported by the NSF CAREER Award.• Developed the theory of human augmentation with mechanically adaptive exoskeletons, predicting 50% increase in running speed, acclaimed by The Guardian and The Conversation US.• Advanced the design of legged robots, exoskeletons, mobile robots, and industrial robots with an intellectual property portfolio featuring four non-provisional and eight provisional patent applications.• Spearheaded the "Robots Teaching Robots" paradigm, underpinned by an NSF DCSD grant, fostering autonomous learning and efficient knowledge transfer between robots.

Singapore

• Conceived the Hardware-in-the-Loop Optimal Control method for optimal control of robots without model-based prediction, supported by an MOE Tier 2 grant by the Singaporean Ministry of Education.• Pioneered an optimization-based framework for variable stiffness actuators, leading to three new types of energy-efficient actuators, supported by the SUTD-MIT International Design Center.• Developed a theory for variable stiffness spring leg exoskeletons, predicting to double human jump height, featured in IEEE's Transactions on Neural Systems and Rehabilitation Engineering.

• Developed the constrained iterative Linear Quadratic Regulator (ciLQR) method, enabling optimal control of complex mechanically adaptive robots.• Received the IEEE Transactions on Robotics Best Paper Award, a top honor recognizing the technical merit and potential impact of the ciLQR method on applications.

Germany

• Validated the constrained iterative Linear Quadratic Regulator method, developed in the EU FP7 STIFF project, on the DLR David.

Nashville, Tennessee, United States

• Devised the ‘constrained Runge-Kutta’ numerical integration method to solve differential-algebraic equations, similar to solving ordinary differential equations using explicit numerical solvers.• Reduced computational errors and maintained error propagation in solving differential-algebraic equations comparable to that in solving ordinary differential equations, leading to more accurate numerical predictions.