8,601 research outputs found
Two Decades of Maude
This paper is a tribute to JosĂ© Meseguer, from the rest of us in the Maude team, reviewing the past, the present, and the future of the language and system with which we have been working for around two decades under his leadership. After reviewing the origins and the language's main features, we present the latest additions to the language and some features currently under development. This paper is not an introduction to Maude, and some familiarity with it and with rewriting logic are indeed assumed.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
The state of MIIND
MIIND (Multiple Interacting Instantiations of Neural Dynamics) is a highly modular multi-level C++ framework, that aims to shorten the development time for models in Cognitive Neuroscience (CNS). It offers reusable code modules (libraries of classes and functions) aimed at solving problems that occur repeatedly in modelling, but tries not to impose a specific modelling philosophy or methodology. At the lowest level, it offers support for the implementation of sparse networks. For example, the library SparseImplementationLib supports sparse random networks and the library LayerMappingLib can be used for sparse regular networks of filter-like operators. The library DynamicLib, which builds on top of the library SparseImplementationLib, offers a generic framework for simulating network processes. Presently, several specific network process implementations are provided in MIIND: the Wilson–Cowan and Ornstein–Uhlenbeck type, and population density techniques for leaky-integrate-and-fire neurons driven by Poisson input. A design principle of MIIND is to support detailing: the refinement of an originally simple model into a form where more biological detail is included. Another design principle is extensibility: the reuse of an existing model in a larger, more extended one. One of the main uses of MIIND so far has been the instantiation of neural models of visual attention. Recently, we have added a library for implementing biologically-inspired models of artificial vision, such as HMAX and recent successors. In the long run we hope to be able to apply suitably adapted neuronal mechanisms of attention to these artificial models
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA
Learning multi-robot coordination from demonstrations
This paper develops a Distributed Differentiable Dynamic Game (DDDG)
framework, which enables learning multi-robot coordination from demonstrations.
We represent multi-robot coordination as a dynamic game, where the behavior of
a robot is dictated by its own dynamics and objective that also depends on
others' behavior. The coordination thus can be adapted by tuning the objective
and dynamics of each robot. The proposed DDDG enables each robot to
automatically tune its individual dynamics and objectives in a distributed
manner by minimizing the mismatch between its trajectory and demonstrations.
This process requires a new distributed design of the forward-pass, where all
robots collaboratively seek Nash equilibrium behavior, and a backward-pass,
where gradients are propagated via the communication graph. We test the DDDG in
simulation with a team of quadrotors given different task configurations. The
results demonstrate the capability of DDDG for learning multi-robot
coordination from demonstrationsComment: 6 figure
Semi-Infinite Structure Analysis with Bimodular Materials with Infinite Element
The modulus of elasticity of some materials changes under tensile and compressive states is simulated by constructing a typical material nonlinearity in a numerical analysis in this paper. The meshless Finite Block Method (FBM) has been developed to deal with 3D semi-infinite structures in the bimodular materials in this paper. The Lagrange polynomial interpolation is utilized to construct the meshless shape function with the mapping technique to transform the irregular finite domain or semi-infinite physical solids into a normalized domain. A shear modulus strategy is developed to present the nonlinear characteristics of bimodular material. In order to verify the efficiency and accuracy of FBM, the numerical results are compared with both analytical and numerical solutions provided by Finite Element Method (FEM) in four examples
Safety-Critical Coordination for Cooperative Legged Locomotion via Control Barrier Functions
This paper presents a safety-critical approach to the coordinated control of
cooperative robots locomoting in the presence of fixed (holonomic) constraints.
To this end, we leverage control barrier functions (CBFs) to ensure the safe
cooperation of the robots while maintaining a desired formation and avoiding
obstacles. The top-level planner generates a set of feasible trajectories,
accounting for both kinematic constraints between the robots and physical
constraints of the environment. This planner leverages CBFs to ensure
safety-critical coordination control, i.e., guarantee safety of the
collaborative robots during locomotion. The middle-level trajectory planner
incorporates interconnected single rigid body (SRB) dynamics to generate
optimal ground reaction forces (GRFs) to track the safety-ensured trajectories
from the top-level planner while addressing the interconnection dynamics
between agents. Distributed low-level controllers generate whole-body motion to
follow the prescribed optimal GRFs while ensuring the friction cone condition
at each end of the stance legs. The effectiveness of the approach is
demonstrated through numerical simulations and experimentally on a pair of
quadrupedal robots.Comment: Under revie
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