Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

First Advisor

Daniel E. Koditschek


How does a robot's body affect what it can do? This thesis explores the question with respect to a body morphology common to biology but rare in contemporary robotics: the presence of a bendable back. In this document, we introduce the Canid and Inu quadrupedal robots designed to test hypotheses related to the presence of a robotic sagittal-plane bending back (which we refer to as a ``spine morphology''). The thesis then describes and quantifies several advantages afforded by this morphological design choice that can be evaluated against its added weight and complexity, and proposes control strategies to both deal with the increase in degrees-of-freedom from the spine morphology and to leverage an increase in agility to reactively navigate irregular terrain. Specifically, we show using the metric of ``specific agility'' that a spine can provides a reservoir of elastic energy storage that can be rapidly converted to kinetic energy, that a spine can augment the effective workspace of the legs without diminishing their force generation capability, and that -- in cases of direct-drive or nearly direct-drive leg actuation -- the spine motors can contribute more work in stance than the same actuator weight used in the legs, but can do so without diminishing the platform's proprioceptive capabilities. To put to use the agility provided by a suitably designed robotic platform, we introduce a formalism to approximate a set of transitional navigational tasks over irregular terrain such as leaping over a gap that lend itself to doubly reactive control synthesis. We also directly address the increased complexity introduced by the spine joint with a modular compositional control framework with nice stability properties that begins to offer insight into the role of spines for steady-state running. A central theme to both the reactive navigation and the modular control frameworks is that analytical tractability is achieved by approximating the modes driving the environmental interactions with constant-acceleration dynamics.

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Robotics Commons