Abstract Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signal cognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.
Intrinsic motivation signals prediction, familiarity (in terms of frequency of state transitions) and stability (in terms of sensor signals to its average) were investigated in @cite_63 in task-independent online visual exploration problems in simulation and on a simple robot.
@cite_63
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Signals from peripheral physiology (e.g., ECG, EMG, and GSR) in conjunction with machine learning techniques can be used for the automatic detection of affective states. The affect detector can be user-independent, where it is expected to generalize to novel users, or user-dependent, where it is tailored to a specific user. Previous studies have reported some success in detecting affect from physiological signals, but much of the work has focused on induced affect or acted expressions instead of contextually constrained spontaneous expressions of affect. This study addresses these issues by developing and evaluating user-independent and user-dependent physiology-based detectors of nonbasic affective states (e.g., boredom, confusion, curiosity) that were trained and validated on naturalistic data collected during interactions between 27 students and AutoTutor, an intelligent tutoring system with conversational dialogues. There is also no consensus on which techniques (i.e., feature selection or classification methods) work best for this type of data. Therefore, this study also evaluates the efficacy of affect detection using a host of feature selection and classification techniques on three physiological signals (ECG, EMG, and GSR) and their combinations. Two feature selection methods and nine classifiers were applied to the problem of recognizing eight affective states (boredom, confusion, curiosity, delight, flow -engagement, surprise, and neutral). The results indicated that the user-independent modeling approach was not feasible; however, a mean kappa score of 0.25 was obtained for user-dependent models that discriminated among the most frequent emotions. The results also indicated that k-nearest neighbor and Linear Bayes Normal Classifier (LBNC) classifiers yielded the best affect detection rates. Single channel ECG, EMG, and GSR and three-channel multimodal models were generally more diagnostic than two--channel models. This paper presents a predictive model of sensor readings for mobile robot. The model predicts sensor readings for given time horizon based on current sensor readings and velocities of wheels assumed for this horizon. Similar models for such anticipation have been proposed in the literature. The novelty of the model presented in the paper comes from the fact that its structure takes into account physical phenomena and is not just a black box, for example a neural network. From this point of view it may be regarded as a semi-phenomenological model. The model is developed for the Khepera robot, but after certain modifications, it may be applied for any robot with distance sensors such as infrared or ultrasonic sensors. Keywords—Mobile robot, sensors, prediction, anticipation. I. INTRODUCTION ODELS of mobile robots usually takes into account only their kinematics and dynamics. Then variables describing the state of the mobile robot are: location coordinates, direction (azimuth) and sometimes velocity. In such models usually readings from the distance sensors are not taken into account. Such measurements, describing the distance from obstacles, are utilized only during generation of the control signals (velocities of the wheels). Moreover, only present measurements are used without any anticipation of measurements. Such anticipation may improve the control quality. If, for example, during the so called behavioral control the "avoid obstacles" rule is activated if the sensory reading exceed assumed threshold, then the control signals might be improved based on the anticipation of the sensory readings. In the literature several attempts of anticipation of sensory readings are reported but they are not serve during the control of the robot but for selection of landmarks (3). For example in articles (2) and (5) an artificial feedforward neural network was proposed as a predictor of further measurements of sensors. The proposed predictor was very simple - it contained only one layer and took as inputs only past readings from neighbouring sensors and did not use wheel velocities of the mobile robot. Such simplified model was good enough for the landmark selection based on the difference between the predicted and real readings but for the control purposes a This thesis investigates risk-sensitive sequential decision-making problems in an uncertain environment. We rst introduce the axiomatic concept of valuation functions that generalize known concepts of risk measures in mathematical nance to cover most of the existing risk related models in various elds, in particular, behavioral economics and cognitive neuroscience. By applying this concept to Markov processes, we construct valuation maps and develop thereby a uni ed framework for incorporating risk into Markov decision processes on general spaces. Within the framework, we study mainly two types of in nite-horizon risk-sensitive criteria, discounted and average valuations, and solve the associated optimization problems by value iteration. For the discounted case, we propose a new discount scheme, which is di erent from the conventional form but consistent with existing literature, while for the average criterion, we state Lyapunov-type stability conditions that generalize known conditions for Markov chains to ensure the existence of solutions to the optimality equation and a geometric convergence rate for the value iteration. Applying a set of valuation functions, called utility-based shortfall, we derive a family of model-free risk-sensitive reinforcement learning algorithms for solving the optimization problems corresponding to risk-sensitive valuations. In addition, we nd that when appropriate utility functions are chosen, agents’ behaviors express key features of human behavior as predicted by prospect theory, for example, di erent risk preferences for gains and losses, as well as the shape of subjective probability curves. As a proof of principle for the applicability of the new algorithms, we apply them to two tasks, 1) to quantify human behavior in a sequential investment task and 2) to perform risk control in simulated algorithmic trading of stocks. In the rst task, the risk-sensitive variant provides a signi cantly better t to the behavioral data and it leads to an interpretation of the subject’s responses which is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals show a signi cant correlation of the risk-sensitive temporal di erence error with BOLD signal change in the ventral striatum. In the second task, our algorithm outperforms the risk-neutral reinforcement learning algorithm by keeping the trading cost at a substantially low level at the spot when the 2010 Flash Crash happened, and signi cantly reducing the risk over the whole test period. In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task.