In order for a mobile robot to accurately interpret its sensations and predict the effects of its actions, it must have accurate models of its sensors and actuators. These models are typically tuned manually, a brittle and laborious process. Autonomous model learning is a promising alternative to manual calibration, but previous work has assumed the presence of an accurate action or sensor model in order to train the other model. This paper presents an adaptation of the Expectation-Maximization (EM) algorithm to enable a mobile robot to learn both its action and sensor model functions, starting without an accurate version of either. The resulting algorithm is validated experimentally both on a Sony Aibo ERS-7 robot and in simulation.

This paper considers two approaches to the problem of vision and self-localization on a mobile robot. In the first approach, the perceptual processing is primarily bottom-up, with visual object recognition entirely preceding localization. In the second, significant top-down information is incorporated, with vision and localization being intertwined. That is, the processing of vision is highly dependent on the robot's estimate of its location. The two approaches are implemented and tested on a Sony Aibo ERS-7 robot, localizing as it walks through a color-coded test-bed domain. This paper's contributions are an exposition of two different approaches to vision and localization on a mobile robot, an empirical comparison of the two methods, and a discussion of the relative advantages of each method.

In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approximator used by such an agent is subject to a number of constraints that may not apply in a traditional supervised learning setting. Many different function approximators exist and are appropriate for different problems. This paper proposes a set of criteria for function approximators for autonomous agents. Additionally, for those problems on which polynomial regression is a candidate technique, the paper presents an enhancement that meets these criteria. In particular, using polynomial regression typically requires a manual choice of the polynomial's degree, trading off between function accuracy and computational and memory efficiency. Polynomial regression with automated degree (PRAD) is a novel function approximation method that uses training data to automatically identify an appropriate degree for the polynomial. PRAD is fully implemented. Empirical tests demonstrate its ability to efficiently and accurately approximate both a wide variety of synthetic functions and real-world data gathered by a mobile robot

This article presents a novel methodology for a robot to autonomously induce models of its actions and sensors called ASAMI (autonomous sensor and actuator model induction). While previous approaches to model learning rely on an independent source of training data, we show how a robot can induce action and sensor models without any well-calibrated feedback. Specifically, the only inputs to the ASAMI learning process are the data the robot would naturally have access to: its raw sensations and knowledge of its own action selections. From the perspective of developmental robotics, our robot’s goal is to obtain self-consistent internal models, rather than to perform any externally defined tasks. Furthermore, the target function of each model-learning process comes from within the system, namely the most current version of another internal system model. Concretely realizing this model-learning methodology presents a number of challenges, and we introduce a broad class of settings in which solutions to these challenges are presented. ASAMI is fully implemented and tested, and empirical results validate our approach in a robotic testbed domain using a Sony Aibo ERS-7 robot.

This paper presents a technique for the Simultaneous Calibration of Action and Sensor Models (SCASM) on a mobile robot. While previous approaches to calibration make use of an independent source of feedback, SCASM is unsupervised, in that it does not receive any well-calibrated feedback about its location. Starting with only an inaccurate action model, it learns accurate relative action and sensor models. Furthermore, SCASM is fully autonomous, in that it operates with no human supervision. SCASM is fully implemented and tested on a Sony Aibo ERS-7 robot.