66,909 research outputs found
Discrete Cosserat Approach for Multi-Section Soft Robots Dynamics
In spite of recent progress, soft robotics still suffers from a lack of
unified modeling framework. Nowadays, the most adopted model for the design and
control of soft robots is the piece-wise constant curvature model, with its
consolidated benefits and drawbacks. In this work, an alternative model for
multisection soft robots dynamics is presented based on a discrete Cosserat
approach, which, not only takes into account shear and torsional deformations,
essentials to cope with out-of-plane external loads, but also inherits the
geometrical and mechanical properties of the continuous Cosserat model, making
it the natural soft robotics counterpart of the traditional rigid robotics
dynamics model. The soundness of the model is demonstrated through extensive
simulation and experimental results for both plane and out-of-plane motions.Comment: 13 pages, 9 figure
Modeling and design of energy efficient variable stiffness actuators
In this paper, we provide a port-based mathematical framework for analyzing and modeling variable stiffness actuators. The framework provides important insights in the energy requirements and, therefore, it is an important tool for the design of energy efficient variable stiffness actuators. Based on new insights gained from this approach, a novel conceptual actuator is presented. Simulations show that the apparent output stiffness of this actuator can be dynamically changed in an energy efficient way
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
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