Biologically Inspired Design (BID), or Biomimicry, is a problem-solving
methodology that applies analogies from nature to solve engineering challenges.
For example, Speedo engineers designed swimsuits based on shark skin. Finding
relevant biological solutions for real-world problems poses significant
challenges, both due to the limited biological knowledge engineers and
designers typically possess and to the limited BID resources. Existing BID
datasets are hand-curated and small, and scaling them up requires costly human
annotations.
In this paper, we introduce BARcode (Biological Analogy Retriever), a search
engine for automatically mining bio-inspirations from the web at scale. Using
advances in natural language understanding and data programming, BARcode
identifies potential inspirations for engineering challenges. Our experiments
demonstrate that BARcode can retrieve inspirations that are valuable to
engineers and designers tackling real-world problems, as well as recover famous
historical BID examples. We release data and code; we view BARcode as a step
towards addressing the challenges that have historically hindered the practical
application of BID to engineering innovation.

通过自然界的生物学类比来解决工程挑战的生物启发式设计（BID）或仿生学是一种解决问题的方法，而 BARcode 是一个用于在网络上自动挖掘生物启发式设计的搜索引擎，能够识别潜在的工程挑战的启发源。

模仿生命：生物启发设计的搜索引擎

Imitation of Life: A Search Engine for Biologically Inspired Design

We present temporally layered architecture (TLA), a biologically inspired
system for temporally adaptive distributed control. TLA layers a fast and a
slow controller together to achieve temporal abstraction that allows each layer
to focus on a different time-scale. Our design is biologically inspired and
draws on the architecture of the human brain which executes actions at
different timescales depending on the environment's demands. Such distributed
control design is widespread across biological systems because it increases
survivability and accuracy in certain and uncertain environments. We
demonstrate that TLA can provide many advantages over existing approaches,
including persistent exploration, adaptive control, explainable temporal
behavior, compute efficiency and distributed control. We present two different
algorithms for training TLA: (a) Closed-loop control, where the fast controller
is trained over a pre-trained slow controller, allowing better exploration for
the fast controller and closed-loop control where the fast controller decides
whether to "act-or-not" at each timestep; and (b) Partially open loop control,
where the slow controller is trained over a pre-trained fast controller,
allowing for open loop-control where the slow controller picks a temporally
extended action or defers the next n-actions to the fast controller. We
evaluated our method on a suite of continuous control tasks and demonstrate the
advantages of TLA over several strong baselines.

该研究提出可适应时间分布式控制的分层式体系结构，该设计模仿人脑的架构，在不同时间尺度上执行动作以适应环境的需求，通过两种不同的算法进行训练，并在连续控制任务中证明比现有方法具有优势。