Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach

Toolbox for entanglement detection and fidelity estimation

The determination of the state fidelity and the detection of entanglement are fundamental problems in quantum information experiments. We investigate how these goals can be achieved with a minimal effort. We show that the fidelity of GHZ and W states can be determined with an effort increasing only linearly with the number of qubits. We also present simple and robust methods for other states, such as cluster states and states in decoherence-free subspaces.Comment: 5 pages, no figures, v3: final version, to appear as a Rapid Communication in PR

Demonstration of Shor's quantum factoring algorithm using photonic qubits

We report an experimental demonstration of a complied version of Shor's algorithm using four photonic qubits. We choose the simplest instance of this algorithm, that is, factorization of N=15 in the case that the period $r=2$ and exploit a simplified linear optical network to coherently implement the quantum circuits of the modular exponential execution and semi-classical quantum Fourier transformation. During this computation, genuine multiparticle entanglement is observed which well supports its quantum nature. This experiment represents a step toward full realization of Shor's algorithm and scalable linear optics quantum computation.Comment: small changes over v2; to appear in PR

Quantum-dot single-photon sources for the quantum internet

High-performance quantum light sources based on semiconductor quantum dots coupled to microcavities are showing their promise in long-distance solid-state quantum networks.Comment: Invited commentary for Nature Nanotechnology 202

Computational Aspects of Optional P\'{o}lya Tree

Optional P\'{o}lya Tree (OPT) is a flexible non-parametric Bayesian model for density estimation. Despite its merits, the computation for OPT inference is challenging. In this paper we present time complexity analysis for OPT inference and propose two algorithmic improvements. The first improvement, named Limited-Lookahead Optional P\'{o}lya Tree (LL-OPT), aims at greatly accelerate the computation for OPT inference. The second improvement modifies the output of OPT or LL-OPT and produces a continuous piecewise linear density estimate. We demonstrate the performance of these two improvements using simulations