13,572 research outputs found

    Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers

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    Real-time learning is crucial for robotic agents adapting to ever-changing, non-stationary environments. A common setup for a robotic agent is to have two different computers simultaneously: a resource-limited local computer tethered to the robot and a powerful remote computer connected wirelessly. Given such a setup, it is unclear to what extent the performance of a learning system can be affected by resource limitations and how to efficiently use the wirelessly connected powerful computer to compensate for any performance loss. In this paper, we implement a real-time learning system called the Remote-Local Distributed (ReLoD) system to distribute computations of two deep reinforcement learning (RL) algorithms, Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO), between a local and a remote computer. The performance of the system is evaluated on two vision-based control tasks developed using a robotic arm and a mobile robot. Our results show that SAC's performance degrades heavily on a resource-limited local computer. Strikingly, when all computations of the learning system are deployed on a remote workstation, SAC fails to compensate for the performance loss, indicating that, without careful consideration, using a powerful remote computer may not result in performance improvement. However, a carefully chosen distribution of computations of SAC consistently and substantially improves its performance on both tasks. On the other hand, the performance of PPO remains largely unaffected by the distribution of computations. In addition, when all computations happen solely on a powerful tethered computer, the performance of our system remains on par with an existing system that is well-tuned for using a single machine. ReLoD is the only publicly available system for real-time RL that applies to multiple robots for vision-based tasks.Comment: Appears in Proceedings of the 2023 International Conference on Robotics and Automation (ICRA). Source code at https://github.com/rlai-lab/relod and companion video at https://youtu.be/7iZKryi1xS

    The metal-weak Milky Way stellar disk hidden in the Gaia-Sausage-Enceladus debris: the APOGEE DR17 view

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    We have for the first time identified the early stellar disk in the Milky Way by using a combination of elemental abundances and kinematics. Using data from APOGEE DR17 and Gaia we select stars in the Mg-Mn-Al-Fe plane with elemental abundances indicative of accreted origin and find stars with both halo-like and disk-like kinematics. The stars with halo-like kinematics lie along a lower sequence in [Mg/Fe], while the stars with disk-like kinematics lie along a higher sequence. Through with asteroseismic observations, we determine the stars with halo-like kinematics are old, 9-11 Gyr and that the more evolved stellar disk is about 1-2 Gyr younger. We show that the in situ fraction of stars on deeply bound orbits is not small, in fact the inner Galaxy likely harbours a genuine in-situ population together with an accreted one. In addition, we show that the selection of Gaia-Sausage-Enceladus in the En-Lz plane is not very robust. In fact, radically different selection criteria give almost identical elemental abundance signatures for the accreted stars.Comment: 32 pages, 19 figures, accepted to Ap

    SignReLU neural network and its approximation ability

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    Deep neural networks (DNNs) have garnered significant attention in various fields of science and technology in recent years. Activation functions define how neurons in DNNs process incoming signals for them. They are essential for learning non-linear transformations and for performing diverse computations among successive neuron layers. In the last few years, researchers have investigated the approximation ability of DNNs to explain their power and success. In this paper, we explore the approximation ability of DNNs using a different activation function, called SignReLU. Our theoretical results demonstrate that SignReLU networks outperform rational and ReLU networks in terms of approximation performance. Numerical experiments are conducted comparing SignReLU with the existing activations such as ReLU, Leaky ReLU, and ELU, which illustrate the competitive practical performance of SignReLU

    Quantum speed limit from a quantum-state-diffusion method

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    Characterizing the most efficient evolution, the quantum speed limit (QSL) plays a significant role in quantum technology. How to generalize the well-established QSL from closed systems to open systems has attracted much attention. In contrast to the previous schemes to derive the QSL from the reduced dynamics of open system, we propose a QSL bound from the point of view of the total system consisting of the open system and its environment using a quantum-state-diffusion method. The application of our scheme to a two-level system reveals that the system possesses an infinite speedup capacity in the noiseless case, which is destroyed by the environment under the Born-Markovian approximation. It is interesting to find that the capacity in the noiseless case is recovered in the non-Markovian dynamics as long as a bound state is formed in the energy spectrum of the total system. Enriching the characterization schemes of the QSL, our result provides an efficient way to control the QSL of open systems

    Weak localization in radiative transfer of acoustic waves in a randomly-fluctuating slab

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    This paper concerns the derivation of radiative transfer equations for acoustic waves propagating in a randomly fluctuating slab (between two parallel planes) in the weak-scattering regime, and the study of boundary effects through an asymptotic analysis of the Wigner transform of the wave solution. These radiative transfer equations allow to model the transport of wave energy density, taking into account the scattering by random heterogeneities. The approach builds on the method of images, where the slab is extended to a full-space, with a periodic map of mechanical properties and a series of sources located along a periodic pattern. Two types of boundary effects, both on the (small) scale of the wavelength, are observed: one at the boundaries of the slab, and one inside the domain. The former impact the entire energy density (coherent as well as incoherent) and is also observed in half-spaces. The latter, more specific to slabs, corresponds to the constructive interference of waves that have reflected at least twice on the boundaries of the slab and only impacts the coherent part of the energy density.Comment: 7 figure

    Virtual reality check: a comparison of virtual reality, screen-based, and real world settings as research methods for HRI

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    To reduce costs and effort, experiments in human-robot interaction can be carried out in Virtual Reality (VR) or in screen-based (SB) formats. However, it is not well examined whether robots are perceived and experienced in the same way in VR and SB as they are in the physical world. This study addresses this topic in a between-subjects experiment, measuring trust and engagement of an interaction with a mobile service robot in a museum scenario. Measures were made in three different settings, either the real world, in VR or in a game-like SB and then compared with an ANOVA. The results indicate, that neither trust nor engagement differ dependent on the experimental setting. The results imply that both VR and SB are eligible ways to explore the interaction with a mobile service robot, if some peculiarities of each medium are taken into account

    Distance education under oppression: The case of Palestinian higher education

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    © 2023 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/educsci13070729This paper draws from both empirical research on an EU-funded project in Palestine and from the lived experiences of Palestinian HE educators. The geopolitical situation is precarious at the best of times in Palestine, where Israel monitors and controls the Palestinians’ right to travel, live and work—even more so if they wish to accomplish these activities abroad—and their access to the internet is never free from surveillance. In these circumstances and under these conditions, distance education has played a crucial role in supporting Palestinian students to develop a global voice. This paper captures some of the educational challenges encountered by Palestinian students and teachers generally in their daily contexts and, more specifically, in their experiences of learning and teaching, and the methods used to overcome these barriers. It draws on multiple sources and on studies re-cently carried out in the field by Palestinian colleagues and will discuss the challenging aspects of learning online from a range of perspectives in each of these studies before offering conclusions and recommendations/implications for other areas of study in situations of oppression. Initial findings indicate that distance education enables a form of continuity in regions exposed and accustomed to extreme and regular disruption. We were also inspired to see throughout responses the values at-tributed to pursuing education by Palestinian educators and their students. The persistence and perseverance reflect a determination that underlines the importance of education as a fundamental human right, national identity and sovereignty, personal source of hope and strength, and oppor-tunity to open one’s world. In our conclusions, we argue for the importance of digital literacy among educators to facilitate the continuity of distance education and finish with some recommendations as to how technologies can ease disruption to ordinary educational service.This research received no external funding

    Information-Theoretic Limits on Compression of Semantic Information

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    As conventional communication systems based on classic information theory have closely approached the limits of Shannon channel capacity, semantic communication has been recognized as a key enabling technology for the further improvement of communication performance. However, it is still unsettled on how to represent semantic information and characterise the theoretical limits. In this paper, we consider a semantic source which consists of a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network. Then we give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence. We further characterise the limits on lossy compression of the semantic source and the corresponding upper and lower bounds of the rate-distortion function. We also investigate the lossy compression of the semantic source with side information at both the encoder and decoder, and obtain the rate distortion function. We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information
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