The Ecology of Open-Ended Skill Acquisition: Computational framework and experiments on the interactions between environmental, adaptive, multi-agent and cultural dynamics

An intriguing feature of the human species is our ability to continuously invent new problems and to proactively acquiring new skills in order to solve them: what is called open-ended skill acquisition (OESA). Understanding the mechanisms underlying OESA is an important scientific challenge in both cognitive science (e.g. by studying infant cognitive development) and in artificial intelligence (aiming at computational architectures capable of open-ended learning). Both fields, however, mostly focus on cognitive and social mechanisms at the scale of an individual’s life. It is rarely acknowledged that OESA, an ability that is fundamentally related to the characteristics of human intelligence, has been necessarily shaped by ecological, evolutionary and cultural mechanisms interacting at multiple spatiotemporal scales. In this thesis, I present a research program aiming at understanding, modelingand simulating the dynamics of OESA in artificial systems, grounded in theories studying its eco-evolutionary bases in the human species. It relies on a conceptual framework expressing the complex interactions between environmental, adaptive, multi-agent and cultural dynamics. Three main research questions are developed and I present a selection of my contributions for each of them.- What are the ecological conditions favoring the evolution of skill acquisition?- How to bootstrap the formation of a cultural repertoire in populations of adaptive agents?- What is the role of cultural evolution in the open-ended dynamics of human skill acquisition?By developing these topics, we will reveal interesting relationships between theories in human evolution and recent approaches in artificial intelligence. This will lead to the proposition of a humanist perspective on AI: using it as a family of computational tools that can help us to explore and study the mechanisms driving open-ended skill acquisition in both artificial and biological systems, as a way to better understand the dynamics of our own species within its whole ecological context. This document presents an overview of my scientific trajectory since the start of my PhD thesis in 2007, the detail of my current research program, a selection of my contributions as well as perspectives for future work

Control strategies for cleaning robots in domestic applications: A comprehensive review:

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches

The synthetic psychology of the self

Synthetic psychology describes the approach of “understanding through building” applied to the human condition. In this chapter, we consider the specific challenge of synthesizing a robot “sense of self”. Our starting hypothesis is that the human self is brought into being by the activity of a set of transient self-processes instantiated by the brain and body. We propose that we can synthesize a robot self by developing equivalent sub-systems within an integrated biomimetic cognitive architecture for a humanoid robot. We begin the chapter by motivating this work in the context of the criteria for recognizing other minds, and the challenge of benchmarking artificial intelligence against human, and conclude by describing efforts to create a sense of self for the iCub humanoid robot that has ecological, temporally-extended, interpersonal and narrative components set within a multi-layered model of mind

Natural task learning through simultaneous language grounding and action learning

Artificial agents and in particular robots, i.e. agents with some form of embodiment, provide nearly unlimited possibilities to support humans in their daily lives by reliably performing hazardous, repetitive, and physically demanding tasks, removing the risk of human errors, and providing social, mental, and physical care as needed, and around-the-clock. However, for this, artificial agents need to be able to communicate with other agents, in particular humans, in a natural and efficient manner, and to autonomously learn new tasks. The most natural way for humans to tell another agent to perform a task or to explain how to perform a task is through natural language. Therefore, artificial agents need to be able to understand natural language, i.e. extract the meanings of words and phrases, which requires words and phrases to be linked to their corresponding percepts through grounding. Theoretically, groundings, i.e. connections between words and percepts, can be manually specified, however, in practice this is not possible due to the complexity and dynamicity of human-centered environments, like private homes or supermarkets, and the ambiguity inherent to natural language, e.g. synonymy and homonymy. Therefore, agents need to be able to autonomously obtain new groundings and continuously update existing groundings to account for changes in the environment and incorporate new information obtained through the agent’s sensors. Furthermore, the obtained groundings should be utilizable to learn new tasks from natural language instructions. Therefore, this thesis proposes a novel framework for simultaneous language grounding and action learning that achieves three main objectives. First, it enables agents to continuously ground synonymous words and phrases without requiring external support by another agent. Second, it enables agents to utilize external support, if available, without depending on it. Finally, it enables agents to utilize previously learned groundings to learn new tasks from language instructions

Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

This manuscript focuses on the processing images from microlens-array based plenoptic cameras. These cameras enable the capturing of the light field in a single shot, recording a greater amount of information with respect to conventional cameras, allowing to develop a whole new set of applications. However, the enhanced information introduces additional challenges and results in higher computational effort. For one, the image is composed of thousand of micro-lens images, making it an unusual case for standard image processing algorithms. Secondly, the disparity information has to be estimated from those micro-images to create a conventional image and a three-dimensional representation. Therefore, the work in thesis is devoted to analyse and propose methodologies to deal with plenoptic images. A full framework for plenoptic cameras has been built, including the contributions described in this thesis. A blur-aware calibration method to model a plenoptic camera, an optimization method to accurately select the best microlenses combination, an overview of the different types of plenoptic cameras and their representation. Datasets consisting of both real and synthetic images have been used to create a benchmark for different disparity estimation algorithm and to inspect the behaviour of disparity under different compression rates. A robust depth estimation approach has been developed for light field microscopy and image of biological samples

Investigating business process elements: a journey from the field of Business Process Management to ontological analysis, and back

Business process modelling languages (BPMLs) typically enable the representation of business processes via the creation of process models, which are constructed using the elements and graphical symbols of the BPML itself. Despite the wide literature on business process modelling languages, on the comparison between graphical components of different languages, on the development and enrichment of new and existing notations, and the numerous definitions of what a business process is, the BPM community still lacks a robust (ontological) characterisation of the elements involved in business process models and, even more importantly, of the very notion of business process. While some efforts have been done towards this direction, the majority of works in this area focuses on the analysis of the behavioural (control flow) aspects of process models only, thus neglecting other central modelling elements, such as those denoting process participants (e.g., data objects, actors), relationships among activities, goals, values, and so on. The overall purpose of this PhD thesis is to provide a systematic study of the elements that constitute a business process, based on ontological analysis, and to apply these results back to the Business Process Management field. The major contributions that were achieved in pursuing our overall purpose are: (i) a first comprehensive and systematic investigation of what constitutes a business process meta-model in literature, and a definition of what we call a literature-based business process meta-model starting from the different business process meta-models proposed in the literature; (ii) the ontological analysis of four business process elements (event, participant, relationship among activities, and goal), which were identified as missing or problematic in the literature and in the literature-based meta-model; (iii) the revision of the literature-based business process meta-model that incorporates the analysis of the four investigated business process elements - event, participant, relationship among activities and goal; and (iv) the definition and evaluation of a notation that enriches the relationships between activities by including the notions of occurrence dependences and rationales

Incorporating Human Expertise in Robot Motion Learning and Synthesis

With the exponential growth of robotics and the fast development of their advanced cognitive and motor capabilities, one can start to envision humans and robots jointly working together in unstructured environments. Yet, for that to be possible, robots need to be programmed for such types of complex scenarios, which demands significant domain knowledge in robotics and control. One viable approach to enable robots to acquire skills in a more flexible and efficient way is by giving them the capabilities of autonomously learn from human demonstrations and expertise through interaction. Such framework helps to make the creation of skills in robots more social and less demanding on programing and robotics expertise. Yet, current imitation learning approaches suffer from significant limitations, mainly about the flexibility and efficiency for representing, learning and reasoning about motor tasks. This thesis addresses this problem by exploring cost-function-based approaches to learning robot motion control, perception and the interplay between them. To begin with, the thesis proposes an efficient probabilistic algorithm to learn an impedance controller to accommodate motion contacts. The learning algorithm is able to incorporate important domain constraints, e.g., about force representation and decomposition, which are nontrivial to handle by standard techniques. Compliant handwriting motions are developed on an articulated robot arm and a multi-fingered hand. This work provides a flexible approach to learn robot motion conforming to both task and domain constraints. Furthermore, the thesis also contributes with techniques to learn from and reason about demonstrations with partial observability. The proposed approach combines inverse optimal control and ensemble methods, yielding a tractable learning of cost functions with latent variables. Two task priors are further incorporated. The first human kinematics prior results in a model which synthesizes rich and believable dynamical handwriting. The latter prior enforces dynamics on the latent variable and facilitates a real-time human intention cognition and an on-line motion adaptation in collaborative robot tasks. Finally, the thesis establishes a link between control and perception modalities. This work offers an analysis that bridges inverse optimal control and deep generative model, as well as a novel algorithm that learns cost features and embeds the modal coupling prior. This work contributes an end-to-end system for synthesizing arm joint motion from letter image pixels. The results highlight its robustness against noisy and out-of-sample sensory inputs. Overall, the proposed approach endows robots the potential to reason about diverse unstructured data, which is nowadays pervasive but hard to process for current imitation learning

Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions