Parsa Mahmoudieh

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Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of selfsupervised tasks that incorporate states, actions, and successors to provide(More)
In this work, we develop a cooperative launching system for a 13.2 gram ornithopter micro-aerial vehicle (MAV), the H<sup>2</sup>Bird, by carrying it on the back of a 32 gram hexapedal millirobot, the VelociRoACH. We determine the necessary initial velocity and pitch angle for take off using force data collected in a wind tunnel and use the VelociRoACH to(More)
This paper details a method for identifying a set of piece-wise affine linear models that can be used for control design for flapping-winged flight. The paper focuses on diving maneuvers as the application for these models. The flight conditions during the dive are segmented into separate dynamically similar regions, and least-squares is used to estimate(More)
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