A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Responding to human full-body gestures embedded in motion data streams.

 This research created a neural-network enabled artificially intelligent performing agent that was able to learn to dance and recognise movement through a rehearsal and performance process with a human dancer. The agent exhibited emergent dance behaviour and successfully engaged in a live, semi-improvised dance performance with the human dancer

Imitation learning through games: theory, implementation and evaluation

Despite a history of games-based research, academia has generally regarded commercial games as a distraction from the serious business of AI, rather than as an opportunity to leverage this existing domain to the advancement of our knowledge. Similarly, the computer game industry still relies on techniques that were developed several decades ago, and has shown little interest in adopting more progressive academic approaches. In recent times, however, these attitudes have begun to change; under- and post-graduate games development courses are increasingly common, while the industry itself is slowly but surely beginning to recognise the potential offered by modern machine-learning approaches, though games which actually implement said approaches on more than a token scale remain scarce. One area which has not yet received much attention from either academia or industry is imitation learning, which seeks to expedite the learning process by exploiting data harvested from demonstrations of a given task. While substantial work has been done in developing imitation techniques for humanoid robot movement, there has been very little exploration of the challenges posed by interactive computer games. Given that such games generally encode reasoning and decision-making behaviours which are inherently more complex and potentially more interesting than limb motion data, that they often provide inbuilt facilities for recording human play, that the generation and collection of training samples is therefore far easier than in robotics, and that many games have vast pre-existing libraries of these recorded demonstrations, it is fair to say that computer games represent an extremely fertile domain for imitation learning research. In this thesis, we argue in favour of using modern, commercial computer games to study, model and reproduce humanlike behaviour. We provide an overview of the biological and robotic imitation literature as well as the current status of game AI, highlighting techniques which may be adapted for the purposes of game-based imitation. We then proceed to describe our contributions to the field of imitation learning itself, which encompass three distinct categories: theory, implementation and evaluation. We first describe the development of a fully-featured Java API - the Quake2 Agent Simulation Environment (QASE) - designed to facilitate both research and education in imitation and general machine-learning, using the game Quake 2 as a testbed. We outline our motivation for developing QASE, discussing the shortcomings of existing APIs and the steps which we have taken to circumvent them. We describe QASE’s network layer, which acts as an interface between the local AI routines and the Quake 2 server on which the game environment is maintained, before detailing the API’s agent architecture, which includes an interface to the MatLab programming environment and the ability to parse and analyse full recordings of game sessions. We conclude the chapter with a discussion of QASE’s adoption by numerous universities as both an undergraduate teaching tool and research platform. We then proceed to describe the various imitative mechanisms which we have developed using QASE and its MatLab integration facilities. We first outline a behaviour model based on a well-known psychological model of human planning. Drawing upon previous research, we also identify a set of believability criteria - elements of agent behaviour which are of particular importance in determining the “humanness” of its in-game appearance. We then detail a reinforcement-learning approach to imitating the human player’s navigation of his environment, centred upon his pursuit of items as strategic goals. In the subsequent section, we describe the integration of this strategic system with a Bayesian mechanism for the imitation of tactical and motion-modelling behaviours. Finally, we outline a model for the imitation of reactive combat behaviours; specifically, weapon-selection and aiming. Experiments are presented in each case to demonstrate the imitative mechanisms’ ability to accurately reproduce observed behaviours. Finally, we criticise the lack of any existing methodology to formally gauge the believability of game agents, and observe that the few previous attempts have been extremely ad-hoc and informal. We therefore propose a generalised approach to such testing; the Bot-Oriented Turing Test (BOTT). This takes the form of an anonymous online questionnaire, an accompanying protocol to which examiners should adhere, and the formulation of a believability index which numerically expresses each agent’s humanness as indicated by its observers, weighted by their experience and the accuracy with which the agents were identified. To both validate the survey approach and to determine the efficacy of our imitative models, we present a series of experiments which use the believability test to evaluate our own imitation agents against both human players and traditional artificial bots. We demonstrate that our imitation agents perform substantially better than even a highly-regarded rule-based agent, and indeed approach the believability of actual human players. Some suggestions for future directions in our research, as well as a broader discussion of open questions, conclude this thesis

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

publishedVersio

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered