Rapidly Learning Generalizable and Robot-Agnostic Tool-Use Skills for a Wide Range of Tasks

Many real-world applications require robots to use tools. However, robots lack the skills necessary to learn and perform many essential tool-use tasks. To this end, we present the TRansferrIng Skilled Tool Use Acquired Rapidly (TRI-STAR) framework for task-general robot tool use. TRI-STAR has three primary components: 1) the ability to learn and apply tool-use skills to a wide variety of tasks from a minimal number of training demonstrations, 2) the ability to generalize learned skills to other tools and manipulated objects, and 3) the ability to transfer learned skills to other robots. These capabilities are enabled by TRI-STAR’s task-oriented approach, which identifies and leverages structural task knowledge through the use of our goal-based task taxonomy. We demonstrate this framework with seven tasks that impose distinct requirements on the usages of the tools, six of which were each performed on three physical robots with varying kinematic configurations. Our results demonstrate that TRI-STAR can learn effective tool-use skills from only 20 training demonstrations. In addition, our framework generalizes tool-use skills to morphologically distinct objects and transfers them to new platforms, with minor performance degradation.

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3D Technology – Additive Manufacturing Applications and Prospects (Vol. 24, No. 9, Full Issue)

Asia-Pacific Biotech News ◽

10.1142/s0219030320001093 ◽

2020 ◽

Vol 24 (09) ◽

Keyword(s):

Additive Manufacturing ◽

3D Printing ◽

Environmental Impact ◽

Real World ◽

Vaccine Development ◽

Health Screening ◽

Wide Range ◽

The World ◽

Real World Applications ◽

Manufacturing Applications

For the month of September 2020, APBN dives into the world of 3D printing and its wide range of real-world applications. Keeping our focus on the topic of the year, the COVID-19 pandemic, we explore the environmental impact of the global outbreak as well as gain insight to the top 5 vaccine platforms used in vaccine development. Discover more about technological advancements and how it is assisting innovation in geriatric health screening.

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Economics of Alliances: The Lessons for Collective Action

Journal of Economic Literature ◽

10.1257/jel.39.3.869 ◽

2001 ◽

Vol 39 (3) ◽

pp. 869-896 ◽

Cited By ~ 165

Author(s):

Todd Sandler ◽

Keith Hartley

Keyword(s):

Collective Action ◽

Public Good ◽

Real World ◽

Pivotal Role ◽

Burden Sharing ◽

Empirical Knowledge ◽

Wide Range ◽

Real World Applications ◽

Further Development

This essay provides an up-to-date summary of the findings of the literature on the economics of alliances. We show that the study of the economics of alliances has played a pivotal role in understanding and applying public good analysis to real-world applications. We establish that the manner in which alliances address burden sharing and allocative issues is related to strategic doctrines, weapon technology, perceived threats, and membership composition. Past contributions are evaluated, and areas needing further development are identified. The theoretical and empirical knowledge gained from the study of alliances is shown to be directly applicable to a wide range of international collectives.

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Eye centre localisation: an unsupervised modular approach

Sensor Review ◽

10.1108/sr-06-2015-0098 ◽

2016 ◽

Vol 36 (3) ◽

pp. 277-286 ◽

Cited By ~ 1

Author(s):

Wenhao Zhang ◽

Melvyn Lionel Smith ◽

Lyndon Neal Smith ◽

Abdul Rehman Farooq

Keyword(s):

Real Time ◽

Real World ◽

Point Of View ◽

Modular Approach ◽

Content Type ◽

Time Performance ◽

Wide Range ◽

Real World Applications ◽

Coarse To Fine ◽

Local Levels

Purpose This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications. Design/methodology/approach A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy. Findings The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows. Originality/value The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications.

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Pivot-based Maximal Biclique Enumeration

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/492 ◽

2020 ◽

Author(s):

Aman Abidi ◽

Rui Zhou ◽

Lu Chen ◽

Chengfei Liu

Keyword(s):

Real World ◽

Search Space ◽

Index Structure ◽

Performance Study ◽

Clique Enumeration ◽

Wide Range ◽

Real World Applications ◽

Real World Datasets ◽

Search Space Pruning ◽

Extensive Performance

Enumerating maximal bicliques in a bipartite graph is an important problem in data mining, with innumerable real-world applications across different domains such as web community, bioinformatics, etc. Although substantial research has been conducted on this problem, surprisingly, we find that pivot-based search space pruning, which is quite effective in clique enumeration, has not been exploited in biclique scenario. Therefore, in this paper, we explore the pivot-based pruning for biclique enumeration. We propose an algorithm for implementing the pivot-based pruning, powered by an effective index structure Containment Directed Acyclic Graph (CDAG). Meanwhile, existing literature indicates contradictory findings on the order of vertex selection in biclique enumeration. As such, we re-examine the problem and suggest an offline ordering of vertices which expedites the pivot pruning. We conduct an extensive performance study using real-world datasets from a wide range of domains. The experimental results demonstrate that our algorithm is more scalable and outperforms all the existing algorithms across all datasets and can achieve a significant speedup against the previous algorithms.

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Unravelling pre-eruptive P-T conditions by machine learning

10.5194/egusphere-egu2020-19028 ◽

2020 ◽

Author(s):

Maurizio Petrelli ◽

Luca Caricchi ◽

Diego Perugini

Keyword(s):

Machine Learning ◽

Real World ◽

The Other ◽

Calibration Data ◽

Wide Range ◽

Volcanic Processes ◽

Real World Applications ◽

Complex Models ◽

Temperature And Pressure ◽

Use Of Models

Clinopyroxene based thermometers and barometers are widely used tools for estimating temperature and pressure conditions under which magmas are stored before eruptions.Several studies reported the development and the application of Clinopyroxene&#8211;liquid geothermobarometers in many different volcanic environments, also warning on the potential pitfall in using overly complex models [e.g., 1 and references therein]. The main drawback in the use of models with a large number of parameters is the potential overfitting of the calibration data, yielding a poor accuracy in real-world applications. On the other hand, simpler models cannot account for the complexity of natural magmatic systems, requiring different calibrations for different magma chemistries [e.g., 2, 3].In the present study, we report on the development of Clinopyroxene and Clinopyroxene-liquid thermometers and barometers in a wide range of P-T-X conditions using Machine Learning (ML) algorithms. To avoid overfitting and demonstrate the robustness of the different methods, we randomly split the dataset into training and validation portions and repeating this procedure up to 10000 times to trace the performance of each of the used algorithms. We compared the performance of ML algorithms with classical and established Clinopyroxene and Clinopyroxene-liquid thermometers and barometers using local and global calibrations. Finally, we applied the obtained thermometers and barometers to real study cases.&#160;[1]&#160;&#160;&#160;&#160;&#160; K. D. Putirka, Thermometers and barometers for volcanic systems, Minerals, Inclusions and Volcanic Processes, 69. 61&#8211;120, 2008.[2]&#160;&#160;&#160;&#160;&#160; D. A. Neave, K. D. Putirka, Am. Mineral., 2017, DOI:10.2138/am-2017-5968.[3]&#160;&#160;&#160;&#160;&#160; M. Masotta, S. Mollo, C. Freda, M. Gaeta, G. Moore, Contrib. to Mineral. Petrol., 2013, DOI:10.1007/s00410-013-0927-9.

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