303 research outputs found

    Sound field separation technique using the principle of double layer patch acoustic radiation modes

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    In order to solve the problems of near-field acoustic holography in applications such as external interference and aperture effects, a sound field separation technique using the principle of double layer patch acoustic radiation modes is proposed in this paper. The radiated acoustic pressures over two planar surfaces at certain distances from the sources are calculated first. Then, the effects resulting from the backscattering interference in non-free sound fields can be eliminated by a double-layer sound field separation technique. Next, data interpolation and extrapolation are performed on the separated data to increase the sound source's pressures on the holographic plane equivalently for holographic images with higher spatial resolution. Simulation and experimental results demonstrate that good agreements can be obtained with few measuring points

    Nonlinear Transport of Graphene in the Quantum Hall Regime

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    We have studied the breakdown of the integer quantum Hall (QH) effect with fully broken symmetry, in an ultra-high mobility graphene device sandwiched between two single crystal hexagonal boron nitride substrates. The evolution and stabilities of the QH states are studied quantitatively through the nonlinear transport with dc Hall voltage bias. The mechanism of the QH breakdown in graphene and the movement of the Fermi energy with the electrical Hall field are discussed. This is the first study in which the stabilities of fully symmetry broken QH states are probed all together. Our results raise the possibility that the v=6 states might be a better target for the quantum resistance standard.Comment: 15 pages,6 figure

    RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot

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    A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations. This feature is attractive for enabling robots to acquire new skills and improving task and motion planning. However, due to limitations in the training dataset, the current focus of the community has mainly been on simple cases, such as push or pick-place tasks, relying solely on visual guidance. In reality, there are many complex skills, some of which may even require both visual and tactile perception to solve. This paper aims to unlock the potential for an agent to generalize to hundreds of real-world skills with multi-modal perception. To achieve this, we have collected a dataset comprising over 110,000 contact-rich robot manipulation sequences across diverse skills, contexts, robots, and camera viewpoints, all collected in the real world. Each sequence in the dataset includes visual, force, audio, and action information. Moreover, we also provide a corresponding human demonstration video and a language description for each robot sequence. We have invested significant efforts in calibrating all the sensors and ensuring a high-quality dataset. The dataset is made publicly available at rh20t.github.ioComment: RSS 2023 workshop on LTAMP. The project page is at rh20t.github.i
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