SUMMARY

Mechanical compliance in robotic systems facilitates safe human-robot interaction and improves robot adaptation to environmental uncertainty. Several promising compliant actuator technologies have emerged from the field of soft robotics, in particular the pneumatic artificial muscle—a soft, lightweight actuator that contracts under pressure. The pneumatic muscle's passive compliance eliminates the need for precise high-bandwidth actuator control to simulate mechanical impedance. However, the pneumatic muscle is limited in practical robot applications—particularly, without sacrificing robot agility—due to several key challenges: development of compatible soft sensors, translation of conventional high-level control and planning techniques to pneumatic muscle-driven systems, and limitations in pneumatic muscle pressurization rate and force generation capabilities. This work seeks to address these challenges, via a threefold approach, to access the benefits of compliant robot actuation while maximizing the robot's dynamic capabilities. The first objective targets the development of a pneumatic muscle design with integrated sensing to enable kinematic and dynamic state estimation of muscle-actuated robots without hindering muscle compliance. The second objective focuses on the construction of a trajectory optimization framework for planning dynamic robot maneuvers using 'burst-inflation' muscle pressure control. Finally, the third objective explores a design optimization strategy utilizing biological joint mechanisms to compensate for pneumatic muscle limitations and maximize robot agility.