Actions in this framework are closed-loop controllers constructed from combinations of sensors, effectors, and potential functions. I will show how robots can use reinforcement learning techniques to acquire sensorimotor programs. The agent then builds a functional model of its interactions with the world as distributions over the acquired skills. In addition, I present two planning algorithms that can reason about a task using the functional models. These algorithms are then applied to a variety of tasks such as object recognition and object manipulation to achieve its objective on two different robot platforms.

Instead of trying to create hardware with ideal energy proportionality, my dissertation evaluates how to use unconventional and probabilistic computing that bends traditional abstractions and interfaces in order to reduce energy consumption while protecting program semantics. My thesis considers four methods that unleash energy otherwise squandered on communication, storage, time keeping, or sensing: 1) CCCP, which provides an energy-efficient storage alternative to local non-volatile storage by relying on cryptographic backscatter radio communication, 2) Half-Wits, which reduces energy consumption by 30% by allowing operation of embedded systems at below-spec supply voltages and implementing NOR flash memory error recovery in firmware rather than strictly in hardware, 3) TARDIS, which exploits the decay properties of SRAM to estimate the duration of a power failure ranging from seconds to several hours depending on hardware parameters, and 4) Nonsensors, which allow operation of analog to digital converters at low voltages without any hardware modifications to the existing circuitry.