The predictive functions that permit humans to infer their body state by sensorimotor integration are critical to perform safe interaction in complex environments. These functions are adaptive and robust to non-linear actuators and noisy sensory information. This paper introduces a computational perceptual model based on predictive processing that enables any multisensory robot to learn, infer and update its body configuration when using arbitrary sensors with Gaussian additive noise. The proposed method integrates different sources of information (tactile, visual and proprioceptive) to drive the robot belief to its current body configuration. The motivation is to provide robots with the embodied perception needed for self-calibration and safe physical human-robot interaction. We formulate body learning as obtaining the forward model that encodes the sensor values depending on the body variables, and we solve it by Gaussian process regression. We model body estimation as minimizing the discrepancy between the robot body configuration belief and the observed posterior. We minimize the variational free energy using the sensory prediction errors (sensed vs expected). In order to evaluate the model we test it on a real multi-sensory robotic arm. We show how different sensor modalities contributions, included as additive errors, improve the refinement of the body estimation and how the system adapts itself to provide the most plausible solution even when injecting strong sensory visuo-tactile perturbations. We further analyse the reliability of the model when different sensor modalities are disabled. This provides grounded evidence about the correctness of the perceptual model and shows how the robot estimates and adjusts its body configuration just by means of sensory information.
In terms of body model learning there is a vast literature on regressors such as locally weighted projection regression @cite_14 , local Gaussian process @cite_15 or infinite experts algorithm @cite_2 . These methods are able to compute the mapping between the sensory input and the configuration of the body and are used to learn forward and inverse kinematics and dynamics. Feed-forward and recurrent nets can also be assimilated for learning body schemas and the needed predictors but relay on supervised information and hundreds of parameters optimization @cite_4 @cite_11 . Unsupervised and self-exploration learning of the body has also been addressed in works like @cite_23 using temporal contingencies. Moreover, biologically plausible sensorimotor learning has been investigated in works like @cite_6 by means of Hebbian-based methods where body calibration can be learnt through sensorimotor mapping. Dynamic Hebbian learning has also been proposed for obtaining intermodal forward models in @cite_26 . Body model free visual detection @cite_29 has been approached as an intermodal inference problem but it is restricted to the camera view of the robot.
@cite_14 @cite_4 @cite_26 @cite_29 @cite_6 @cite_23 @cite_2 @cite_15 @cite_11
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Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high-dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally efficient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least squares regression. We discuss when and how local learning techniques can successfully work in high-dimensional spaces and review the various techniques for local dimensionality reduction before finally deriving the LWPR algorithm. The properties of LWPR are that it (1) learns rapidly with second-order learning methods based on incremental training, (2) uses statistically sound stochastic leave-one-out cross validation for learning without the need to memorize training data, (3) adjusts its weighting kernels based on only local information in order to minimize the danger of negative interference of incremental learning, (4) has a computational complexity that is linear in the number of inputs, and (5) can deal with a large number of—possibly redundant—inputs, as shown in various empirical evaluations with up to 90 dimensional data sets. For a probabilistic interpretation, predictive variance and confidence intervals are derived. To our knowledge, LWPR is the first truly incremental spatially localized learning method that can successfully and efficiently operate in very high-dimensional spaces. The development and expression of many higher-level cognitive functions, such as imitation, spatial perception and tool-use, relies on a multi-modal representation of the body known as the body schema. Although many studies support the hypothesis that the body schema is adaptive and alterable throughout ontogenetic development, the mechanisms underlying its plasticity have yet to be clarified. Here, we argue that the temporal integration of multisensory information is a plausible candidate mechanism to explain how manipulated objects (e.g., tools) can become incorporated into the body schema. To demonstrate the validity of our idea, we introduce a model of body schema adaptation instantiated in a small-sized, table-top, tool-using humanoid. The robot's task is to learn to reach for and touch a visually salient distant object, first with its 'bare' hand and then — using the acquired know-how — with a reach-extending tool (a stick). Our experimental results show that in order to successfully causally relate... Sensorimotor control and learning are fundamental prerequisites for cognitive development in humans and animals. Evidence from behavioural sciences and neuroscience suggests that motor and brain development are strongly intertwined with the experiential process of , where internal body representations are formed and maintained over time. In order to guide our movements, our brain must hold an internal model of our body and constantly monitor its configuration state. How can sensorimotor control using such low-level body representations enable the development of more complex cognitive and motor capabilities? Although a clear answer has still not been found for this question, several studies suggest that processes of mental simulation of action-perception loops are likely to be executed in our brain and are dependent on internal body representations. Therefore, the capability to re-enact sensorimotor experience might represent a key mechanism behind the implementation of higher cognitive capabilities, such as behaviour recognition, arbitration and imitation, sense of agency and self-other distinction. Addressed mainly to researchers on autonomous motor and mental development in artificial agents, this work aims at gathering the latest development in the study on exploration behaviours, on internal body representations, on internal models, and on mechanisms for internal sensorimotor simulations. Relevant studies in human and animal sciences are discussed and a parallel to similar investigations in robotics is presented. We address self-perception and object discovery by integrating multimodal tactile, proprioceptive and visual cues. Considering sensory signals as the only way to obtain relevant information about the environment, we enable a humanoid robot to infer potential usable objects relating visual self-detection with tactile cues. Hierarchical Bayesian models are combined with signal processing and protoobject artificial attention to tackle the problem. Results show that the robot is able to: (1) discern between inbody and outbody sources without using markers or simplified segmentation; (2) accurately discover objects in the reaching space; and (3) discriminate real objects from visual artefacts, aiding scene understanding. Furthermore, this approach reveals the need for several layers of abstraction for achieving agency and causality due to the inherent ambiguity of the sensory cues. Recent progresses of ultrasound imaging technology have led observations of fet al intrauterine behavior and a perspective of intrauterine learning. Understanding fet al behavior in uterus is important for medical cares for prenatal infants, because the intervention like “nesting” or “swaddling” in NICU (Neonatal Intensive Care Unit) is based on a perspective of intrauterine learning. However, fet al behavior is not explained sufficiently by the perspective. In this study, we have proposed a hypothesis in which two fet al behaviors, Isolated leg arm movements and hand and face contact, emerge within self-organization of interaction among an uterine environment, a fet al body, and a nervous system. through tactile sensation in uterus. We have conducted computer experiments with a simple musculoskelet al model in uterus and a whole body fet al musculoskelet al model with tactile for the hypothesis. We confirmed that tactile sensation induces motions in the experiments of the simple model, and the fet al model with human like tactile distribution have behaved with the two motions similar to real fet al behaviors. Our experiments indicated that fet al intrauterine learning is possibly core concept for the fet al motor development. This paper addresses the problem of self-detection by a robot. The paper describes a methodology for autonomous learning of the characteristic delay between motor commands (efferent signals) and observed movements of visual stimuli (afferent signals). The robot estimates its own efferent-afferent delay from self-observation data gathered while performing motor babbling, i.e., random rhythmic movements similar to the primary circular reactions described by Piaget. After the efferent-afferent delay is estimated, the robot imprints on that delay and can later use it to successfully classify visual stimuli as either ???self??? or ???other.??? Results from robot experiments performed in environments with increasing degrees of difficulty are reported. This paper proposes a supervised algorithm for online learning of input-output relations that is particularly suitable to simultaneously learn the forward and inverse kinematics of general manipulators — the multi-valued nature of the inverse kinematics of serial chains and forward kinematics of parallel manipulators makes it infeasible to apply state-of-the-art learning techniques to these problems, as they typically assume a single-valued function to be learned. The proposed algorithm is based on a generalized expectation maximization approach to fit an infinite mixture of linear experts to an online stream of data samples, together with an outlier probabilistic model that dynamically grows the number of linear experts allocated to the mixture, this way controlling the complexity of the resulting model. The result is an incremental, online and localized learning algorithm that performs nonlinear, multivariate regression on multivariate outputs by approximating the target function by a linear relation within each expert input domain, which can directly provide forward and inverse multi-valued estimates. The experiments presented in this paper show that it can achieve, for single-valued functions, a performance directly comparable to state-of-the-art online function approximation algorithms, while additionally providing inverse predictions and the capability to learn multi-valued functions in a natural manner. To our knowledge this is a distinctive property of the algorithm presented in this paper. Learning in real-time applications, e.g., online approximation of the inverse dynamics model for model-based robot control, requires fast online regression techniques. Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP). The training data is partitioned in local regions, for each an individual GP model is trained. The prediction for a query point is performed by weighted estimation using nearby local models. Unlike other GP approximations, such as mixtures of experts, we use a distance based measure for partitioning of the data and weighted prediction. The proposed method achieves online learning and prediction in real-time. Comparisons with other nonparametric regression methods show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and nu-SVR. Developmental robotics suggests that the forward and inverse kinematics should be learned through a sensory-motor mapping, instead of being programmed in advance. Motor babbling and goal babbling are two common approaches to generate training samples used to acquire a sensory-motor mapping. Motor babbling typically needs a considerable amount of training data and time to acquire a sufficient mapping, while goal babbling poses difficulties on how to select appropriate goals. In this paper, we propose a neurobiologically-inspired system to progressively learn a sensory-motor mapping bootstrapped from a simple constrained DOF exploration, which generates much less training data than motor babbling. Our proposed system is designed according to two neurobiologically-inspired paradigms: spatiotemporal prediction and uniformity. The spatiotemporal prediction capability facilitates the acquisition of sensory-motor mappings with less amount of training data on the one hand, and facilitates robust behaviour on the other hand. The uniform system design structure is the foundation for building a scalable architecture for cognitive development. We use an improved version of our predictive action selector (PAS) as building block of our system. We validate a PAS on a 2 DOF robot head where the robot learns object tracking and evading. Then we validate a second PAS on a 5 DOF arm where it learns reaching.