145,673 research outputs found

    Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking

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    In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties). The goal is to leverage knowledge from the source robot such that the target robot achieves high-accuracy trajectory tracking on arbitrary trajectories from the first attempt with minimal data recollection and training. Most existing approaches for multi-robot knowledge transfer are based on post-analysis of datasets collected from both robots. In this work, we study the feasibility of impromptu transfer of models across robots by learning an error prediction module online. In particular, we analytically derive the form of the mapping to be learned by the online module for exact tracking, propose an approach for characterizing similarity between robots, and use these results to analyze the stability of the overall system. The proposed approach is illustrated in simulation and verified experimentally on two different quadrotors performing impromptu trajectory tracking tasks, where the quadrotors are required to accurately track arbitrary hand-drawn trajectories from the first attempt.Comment: European Control Conference (ECC) 201

    Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization

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    Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains. Both black-box and white-box approaches have been used to either replicate the model itself or to craft examples which cause the model to fail. In this work, we propose a framework which uses multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems. We apply this framework on two ASR systems: Deepspeech and Kaldi-ASR, which increases the Word Error Rates (WER) of these systems by upto 980%, indicating the potency of our approach. During both un-targeted and targeted attacks, the adversarial samples maintain a high acoustic similarity of 0.98 and 0.97 with the original audio.Comment: Published in Interspeech 201

    Process-oriented Iterative Multiple Alignment for Medical Process Mining

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    Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.Comment: accepted at ICDMW 201

    Decentralization of Multiagent Policies by Learning What to Communicate

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    Effective communication is required for teams of robots to solve sophisticated collaborative tasks. In practice it is typical for both the encoding and semantics of communication to be manually defined by an expert; this is true regardless of whether the behaviors themselves are bespoke, optimization based, or learned. We present an agent architecture and training methodology using neural networks to learn task-oriented communication semantics based on the example of a communication-unaware expert policy. A perimeter defense game illustrates the system's ability to handle dynamically changing numbers of agents and its graceful degradation in performance as communication constraints are tightened or the expert's observability assumptions are broken.Comment: 7 page

    Health initiatives to target obesity in surface transport industries: review and implications for action

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    Lifestyle-related chronic diseases pose a considerable burden to the individual and the wider society, with correspondingly negative effects on industry. Obesity is a particular problem for the Australasian road and rail industries where it is associated with specific cardiac and fatigue-related safety risks, and levels are higher than those found in the general population. Despite this recognition, and the introduction of National Standards, very little consensus exists regarding approaches to preventative health for surface transport workers. A review of evidence regarding effective health promotion initiatives is urgently needed to inform best practice in this cohort. This review draws together research informing the scope and effectiveness of health promotion programs, initiatives and interventions targeting overweight and obesity in safety critical surface transport domains including the truck, bus and rail industries. A number of health interventions demonstrated measurable successes, including incentivising, peer mentoring, verbal counselling, development of personalised health profiles, and offer of healthier on-site food choices – some of which also resulted in sizeable return on investment over the long term. &nbsp

    Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles

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    There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3D marine habitat mapping, require that a number of heterogeneous vehicles operate simultaneously in small areas, often in close proximity of each other. In these circumstances safety, reliability, and efficient multiple vehicle operation are key ingredients for mission success. Additionally, the deployment and operation of multiple AMVs at sea are extremely costly in terms of the logistics and human resources required for mission supervision, often during extended periods of time. These costs can be greatly minimized by automating the deployment and initial steering of a vehicle fleet to a predetermined configuration, in preparation for the ensuing mission, taking into account operational constraints. This is one of the core issues addressed in the scope of the Widely Scalable Mobile Underwater Sonar Technology project (WiMUST), an EU Horizon 2020 initiative for underwater robotics research. WiMUST uses a team of cooperative autonomous ma- rine robots, some of which towing streamers equipped with hydrophones, acting as intelligent sensing and communicat- ing nodes of a reconfigurable moving acoustic network. In WiMUST, the AMVs maintain a fixed geometric formation through cooperative navigation and motion control. Formation initialization requires that all the AMVs start from scattered positions in the water and maneuver so as to arrive at required target configuration points at the same time in a completely au- tomatic manner. This paper describes the decoupled prioritized vehicle motion planner developed in the scope of WiMUST that, together with an existing system for trajectory tracking, affords a fleet of vehicles the above capabilities, while ensuring inter- vehicle collision and streamer entanglement avoidance. Tests with a fleet of seven marine vehicles show the efficacy of the system planner developed.Peer reviewe

    An adaptation reference-point-based multiobjective evolutionary algorithm

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics
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