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Showing 1–6 of 6 results
Advanced filters: Author: Sam Kriegman Clear advanced filters
  • Inspired by many examples in nature where organisms change shape to concur environments, there is much interest in designing robots that are capable of shape change. Shah et al. demonstrate a method for automatically discovering shape and gait changes for soft robots that can adapt to different terrains.

    • Dylan S. Shah
    • Joshua P. Powers
    • Rebecca Kramer-Bottiglio
    Research
    Nature Machine Intelligence
    Volume: 3, P: 51-59
  • Tired of training neural networks? Try optimizing virtual creatures instead.

    • Sam Kriegman
    Comments & Opinion
    Nature Machine Intelligence
    Volume: 1, P: 492
  • It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

    • Dhireesha Kudithipudi
    • Mario Aguilar-Simon
    • Hava Siegelmann
    Reviews
    Nature Machine Intelligence
    Volume: 4, P: 196-210