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Volume 1 Issue 8, August 2019

A metric for task-oriented grasping

Robots that can work together safely with humans in unstructured environments need to be able to effectively manipulate objects for a variety of tasks. Much attention has been focused on the challenge of grasping different objects with robotic hands. However, Ortenzi et al. argue that grasping depends on the task. They develop a new metric that measures the success of robot manipulation focused on the goal of the task itself, for instance handing over an object, rather that the action of grasping.

See Ortenzi et al.

Image: Elastico Disegno snc - Prensilia SrlCover Design: Karen Moore

Volume 1 Issue 8

Editorial

  • As machine learning methods are adopted across the scientific community, strong code sharing and reviewing practices are required. Our policy mandates that code essential to the main results is made available to reviewers, and to readers on publication. Our partnership with Code Ocean helps authors and reviewers navigate this process.

    Editorial

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Comment & Opinion

  • To create less harmful technologies and ignite positive social change, AI engineers need to enlist ideas and expertise from a broad range of social science disciplines, including those embracing qualitative methods, say Mona Sloane and Emanuel Moss.

    • Mona Sloane
    • Emanuel Moss
    Comment
  • Deepfakes are a new dimension of the fake news problem. The criminal misuse of this technology poses far-reaching challenges and can threaten national security. Technological and governance solutions are needed to address this.

    • Irakli Beridze
    • James Butcher
    Comment
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Books & Arts

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News & Views

  • DeepMind’s AlphaFold recently demonstrated the potential of deep learning for protein structure prediction. DeepFragLib, a new protein-specific fragment library built using deep neural networks, may have advanced the field to the next stage.

    • Guo-Wei Wei
    News & Views
  • To prepare robots for working autonomously under real-world conditions, their resilience and capability to recover from damage needs to improve radically. A fresh take on robot design suggests that instead of adapting the robotic control strategy, we could enable robots to change their physical bodies to recover more effectively from damage.

    • Helmut Hauser
    News & Views
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Reviews

  • Traditional robotic grasping focuses on manipulating an object, often without considering the goal or task involved in the movement. The authors propose a new metric for success in manipulation that is based on the task itself.

    • V. Ortenzi
    • M. Controzzi
    • P. Corke
    Perspective
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Research

  • An approach to protein structure prediction is to assemble candidate structures from template fragments, which are extracted from known protein structures. Wang et al. demonstrate that combining deep neural network architectures with a relatively small but high-resolution fragment dataset can improve the quality of the sample fragment libraries used for protein structure prediction.

    • Tong Wang
    • Yanhua Qiao
    • Haipeng Gong
    Article
  • When neural networks are retrained to solve more than one problem, they tend to forget what they have learned earlier. Here, the authors propose orthogonal weights modification, a method to avoid this so-called catastrophic forgetting problem. Capitalizing on such an ability, a new module is introduced to enable the network to continually learn context-dependent processing.

    • Guanxiong Zeng
    • Yang Chen
    • Shan Yu
    Article
  • Deep neural networks can contain arbitrary mathematical operators, as long as they are derivable. The authors investigate how knowledge about a problem can be incorporated into machine learning through the use of operators that are related to the problem.

    • Andreas K. Maier
    • Christopher Syben
    • Silke Christiansen
    Article
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Challenge Accepted

  • As nations come together in Tokyo next summer to celebrate the spirit of human potential in the 2020 Olympic Games, they will have a chance to take part in another international competition hosted by Japan soon after, this time with challenges designed for robot contenders.

    • Liesbeth Venema
    Challenge Accepted
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Amendments & Corrections

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