Novelty detection and learning drives

This document presents Deliverable 5.1 of the IM-CLeVeR (Intrinsically Motivated Cumulative Learning Versatile Robots) EU FP7 project. It represents one of two deliverables from Workpackage 5 (Novelty Detection and Drives for Autonomous Learning)

An evolutionary behavioral model for decision making

For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud the same evolutionary mechanism is able to assemble quite\ud complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies

Opportunistic Multi-sensor Fusion for Robust Navigation in Smart Environments

Proceedings of: Workshop on User-Centric Technologies and Applications (CONTEXTS 2011), Salamanca, April 6-8, 2011This paper presents the design of a navigation system for multiple autonomous robotic platforms. It performs multisensor fusion using a Monte Carlo Bayesian filter, and has been designed to maximize information acquisition. Apart from sensors equipped in the mobile platform, the system can dynamically integrate observations from friendly external sensing entities, increasing robustness and making it suitable for both indoor and outdoor operation. A multi-agent layer manages the information acquisition process, making it transparent for the core filtering solution. As a proof of concept, some preliminary results are presented over a real platform using the part of the system specialized in outdoor navigationThis work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-02.publicad

Functions and mechanisms of intrinsic motivations: the knowledge versus competence distinction

Mammals, and humans in particular, are endowed with an exceptional capacity for cumulative learning. This capacity crucially de- pends on the presence of intrinsic motivations, i.e. motivations that are not directly related to an organism\u27s survival and reproduction but rather to its ability to learn. Recently, there have been a number of attempts to model and reproduce intrinsic motivations in artificial systems. Different kinds of intrinsic motivations have been proposed both in psychology and in machine learning and robotics: some are based on the knowl- edge of the learning system, while others are based on its competence. In this contribution we discuss the distinction between knowledge-based and competence-based intrinsic motivations with respect to both the functional roles that motivations play in learning and the mechanisms by which those functions are implemented. In particular, after arguing that the principal function of intrinsic motivations consists in allowing the development of a repertoire of skills (rather than of knowledge), we suggest that at least two different sub-functions can be identified: (a) discovering which skills might be acquired and (b) deciding which skill to train when. We propose that in biological organisms knowledge-based intrinsic motivation mechanisms might implement the former function, whereas competence-based mechanisms might underly the latter one

On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehousePartial funding for open access charge: Universidad de Málaga. This work has been partially developed within SA3IR (an experiment funded by EU H2020 ESMERA Project under Grant Agreement 780265), the project RTI2018-099522-B-C4X, funded by the Gobierno de España and FEDER funds, and the B1-2021_26 project, funded by the University of Málaga