This article e xplore s ane approach to le rning by imitation and traje5 ory formation byre reC-- ting move - me ts as mixture s of nonline r di#e e tialeC-- tions with we ll-de fine d attractor dynamics. An obseC e move me nt is approximate by finding a be5 fit of the mixture mode to its data by areC---fl--k e le5R square reC--6:---k2 te hnique In contrast to non-autonomous move me t re pr e se tationslike spline7 the re sultant moveC-- t plan r e mains an autonomous se of nonlineC di#eCG tial ek ations that forms a control policy which is robust to strong ek e rnal pe rturbations and that can be modifie by additional pe rce tual variable s. This move me nt policy r e mains the same for a give targe5 r e ardlefl of the initial conditions, and canek5 ly be reR se for ne w targe s. We e aluate the traje5 ory formation syste (TFS) in the conte xt of a humanoid robot simulation that is part of the Virtual Traine r (VT) proje5 , which aims at supe rvising reR bilitatione xe cise in stroke:G tie ts. A typical re habilitatione xe cise was colle6Gfl with a Sarcos SeC suit, ade:C: to re5 rd joint angular move me t from human subje7C7 and approximate and reC5 duce with our imitation te hniqueC Our re sults deC nstrate that multijoint human move me ts can be e56 de succeGk2CC6 , and that thissyste allows robust modifications of the move - me nt policy through eke rnal variable s.

A new cognitive architecture for artificial vision is proposed. The architecture, aimed at an autonomous intelligent system, is cognitive in the sense that several cognitive hypotheses have been postulated as guidelines for its design. The first one is the existence of a conceptual representation level between the subsymbolic level, that processes sensory data, and the linguistic level, that describes scenes by means of a high-level language. The conceptual level plays the role of the interpretation domain for the symbols at the linguistic levels. A second cognitive hypothesis concerns the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level: the exploration process of the perceived scene is driven by linguistic and associative expectations. This link is modeled as a timedelay attractor neural network. Results are reported obtained by an experimental implementation of the architecture.

Can one develop an abstract description of the dynamics of pattern generators that provides quantitative insight into their operation? We explored this question by examining the dynamics of a model central pattern generator that was created using an evolutionary algorithm. We propose an abstract description based on the concept of a dynamical module, a set of neurons that simultaneously make their transitions from one quasistable state to another while the synaptic inputs that they receive remain essentially constant, thus temporarily reducing the dimensionality of the circuit dynamics. Using the mathematical tools of dynamical systems theory, we describe a method for identifying dynamical modules, and demonstrate that this concept can be used to quantitatively characterize constraints on neural architecture, account for phase durations, and predict the effects of parameter changes. Moreover, this abstract description reveals coordinated parameter changes that leave the overall circuit...

ABSTRACT Cyclic patterns ofmotor neuron activity are involved in the production ofmany rhythmic movements, such as walking, swimming, and scratching. These movements are controlled by neural circuits referred to as central pattern generators (CPGs). Some of these circuits function in the absence of both internal pacemakers and external feedback. We describe an associative neural network model whose dynamic behavior is similar to that of CPGs. The theory predicts the strength of all possible connections between pairs ofneurons on the basis ofthe outputs oftheCPG. It also allows themean operating levels ofthe neurons tobededuced from themeasured synaptic strengthsbetween the pairs of neurons. We apply our theory to theCPG controlling escape swimming in the mollusk Tritonia diomedea. The basic rhythmic behavior is shown to be consistent with a simplified model that approximates neurons as threshold units and slow synaptic responses as elementary time delays. The model we describe may have relevance to other fixed action behaviors, as well as to the learning, recall, and recognition oftemporally ordered information.

We present control policies (CPs) based on nonlinear differential equations with well-defined attractor dynamics for learning by imitation and trajectory formation in humanoid robotics. The CPs can fit complex movements (presented as joint-angle trajectories) using incremental locally-weighted regression techniques. Being implemented as autonomous nonlinear differential equations gives the CPs interesting properties such as robustness against strong external perturbations and the ability to generate movements which can easily be modified by additional perceptual variables and re-used for new targets. We evaluate the CPs in the context of a humanoid robot. Typical reaching movements were collected with a Sarcos Sensuit, a device to record joint angular movement from human subjects, and approximated and reproduced with our imitation techniques. Our results demonstrate (a) that multi-joint human movements can be encoded successfully by the CPs, (b) that a learned movement policy can readily be reused to produce robust trajectories towards different targets, (c) that a policy fitted for one particular target provides a good predictor of human reaching movements towards neighboring targets, and (d) that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar. 1

A framework for high-level representations in computer vision architectures is described. The framework is based on the notion of conceptual space proposed by Gardenfors [12]. This approach allows to define a conceptual semantics for the symbolic representations of the vision system. In this way the semantics of the symbols can be grounded on the data coming from the sensors. In addition, the proposed approach generalizes the most popular representation frameworks adopted in computer vision. 1 Introduction According to Marr [17], computer vision is the process that, starting from bidimensional images, automatically discovers what is present in the external world and where it is. Computer vision is an information-processing task that receives in input raw and low structured data (the images acquired by a video camera), and gives as its output highly structured data (suitable symbolic descriptions of the scene). Such data are crucial for the e#ective autonomy of a moving robot [...

In recent years, computational biologists have shown through simulation that small neural networks with fixed connectivity are capable of producing multiple output rhythms in response to transient inputs. It is believed that such networks may play a key role in certain biological behaviors such as dynamic gait control. In this paper, we present a novel method for designing continuous-time recurrent neural networks (CTRNNs) that contain multiple embedded limit cycles, and we show that it is possible to switch the networks between these embedded limit cycles with simple transient inputs. We also describe the design and testing of a fully integrated four-neuron CTRNN chip that is used to implement the neural network pattern generators. We provide two example multipattern generators and show that the measured waveforms from the chip agree well with numerical simulations.