Uniaxial materials whose axial and tangential permittivities have opposite signs are referred to as indefinite or hyperbolic media. While hyperbolic responses are normally achieved with metamaterials, hexagonal boron nitride (hBN) naturally possesses this property due to the anisotropic phonons in the mid-infrared. Using scattering-type scanning near-field optical microscopy, we studied polaritonic phenomena in hBN. We performed infrared nano-imaging of highly confined and low-loss hyperbolic phonon polaritons in hBN. The polariton wavelength was shown to be governed by the hBN thickness according to a linear law persisting down to few atomic layers [1]. Additionally, we carried out the modification of hyperbolic response in meta-structures comprised of a mononlayer graphene deposited on hBN [2]. Electrostatic gating of the top graphene layer allows for the modification of wavelength and intensity of hyperbolic phonon polaritons in bulk hBN. The physics of the modification originates from the plasmon-phonon coupling in the hyperbolic medium. Furthermore, we demonstrated the “hyperlens” for subdiffractional focusing and imaging using a slab of hBN [3]. References [1] S. Dai et al., Science, 343, 1125 (2014). [2] S. Dai et al., Nature Nanotechnology, 10, 682 (2015). [3] S. Dai et al., Nature Communications, 6, 6963 (2015).
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In this research, time optimal control is considered for the hit motion of a badminton robot during a serve operation. For this task the racket always starts at rest in a given position and has to move to a target state, defined by a target position and a non-zero target velocity. The goal is to complete this motion in as little time as possible, yet without violating bounds on the actuator. To find controllers satisfying these requirements, a reinforcement learning approach is implemented, using a Natural Actor-Critic (NAC) reinforcement learning algorithm. This approach is experimentally shown to yield the desired robot motions after about 200 trials. Next to this model-free learning approach, the control signals obtained with a model-based optimization are also applied to the robot. The results achieved with both approaches are compared, and a thorough analysis is presented, highlighting the properties of each approach, as well as their advantages and drawbacks.@en