2,845 research outputs found
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Ball catching by a puma arm : a nonlinear dynamical systems approach
We present an attractor based dynamics that autonomously
generates temporally discrete movements and movement
sequences stably adapted to changing online sensory information.
Autonomous differential equations are used to formulate
a dynamical layer with either stable fixed points or a stable limit
cycle. A neural competitive dynamics switches between these two
regimes according to sensorial context and logical conditions. The
corresponding movement states are then converted by simple
coordinate transformations into spatial positions of a robot arm.
Movement initiation and termination is entirely sensor driven.
In this article, the dynamic architecture was changed in order
to cope with unreliable sensor information by including this
information in the vector field.
We apply this architecture to generate timed trajectories for
a Puma arm which must catch a moving ball before it falls over
a table, and return to a reference position thereafter. Sensory
information is provided by a camera mounted on the ceiling
over the robot. We demonstrate that the implemented decisionmechanism
is robust to noisy sensorial information. Further, a
flexible behavior is achieved. Flexibility means that if the sensorial
context changes such that the previously generated sequence is
no longer adequate, a new sequence of behaviors, depending on
the point at which the changed occurred and adequate to the
current situation emerges
A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems
Bio-hybrid systems---close couplings of natural organisms with
technology---are high potential and still underexplored. In existing work,
robots have mostly influenced group behaviors of animals. We explore the
possibilities of mixing robots with natural plants, merging useful attributes.
Significant synergies arise by combining the plants' ability to efficiently
produce shaped material and the robots' ability to extend sensing and
decision-making behaviors. However, programming robots to control plant motion
and shape requires good knowledge of complex plant behaviors. Therefore, we use
machine learning to create a holistic plant model and evolve robot controllers.
As a benchmark task we choose obstacle avoidance. We use computer vision to
construct a model of plant stem stiffening and motion dynamics by training an
LSTM network. The LSTM network acts as a forward model predicting change in the
plant, driving the evolution of neural network robot controllers. The evolved
controllers augment the plants' natural light-finding and tissue-stiffening
behaviors to avoid obstacles and grow desired shapes. We successfully verify
the robot controllers and bio-hybrid behavior in reality, with a physical setup
and actual plants
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