Michael D. Taylor

Carnegie Mellon University, Australian Regenerative Medicine Institute

Papers

Total Citations

129

H-Index

About

Michael D. Taylor is a robotics researcher whose work spans energy-efficient actuation, neuromuscular control, and AI-driven energy harvesting. His most influential contribution is the concept of a clutched parallel elastic actuator, introduced in his 2012 paper (111 citations), which demonstrated how passive-elastic elements could be selectively engaged and disengaged to reduce energy consumption and torque demands in powered prosthetic and robotic legs—a key step toward more efficient, lifelike locomotion. Taylor also developed a testbed for robotic neuromuscular controllers, challenging traditional centralized control by drawing inspiration from biological systems to improve dexterity in human-integrated assistive devices. More recently, he has applied deep reinforcement learning to high-degree-of-freedom robotics for maximum solar energy tracking, addressing critical failure modes in autonomous environmental sensing. With over a decade of work bridging hardware design and intelligent control, Taylor’s research continues to push the boundaries of how robots interact with both humans and the natural environment, offering practical pathways toward more adaptive, energy-savvy robotic systems.