Longterm Generalized Actions for Smart, Autonomous Robot Agents

Creating intelligent artificial systems, and in particular robots, that improve themselves just like humans do is one of the most ambitious goals in robotics and machine learning. The concept of robot experience exists for some time now, but has up to now not fully found its way into autonomous robots. This thesis is devoted to both, analyzing the underlying requirements for enabling robot learning from experience and actually implementing it on real robot hardware. For effective robot learning from experience I present and discuss three main requirements: (a ) Clearly expressing what a robot should do, on a vague, abstract level I introduce Generalized Plans as a means to express the intention rather than the actual action sequence of a task, removing as much task specific knowledge as possible. (a ) Defining, collecting, and analyzing robot experiences to enable robots to improve I present Episodic Memories as a container for all collected robot experiences for any arbitrary task and create sophisticated action (effect) prediction models from them, allowing robots to make better decisions. (a ) Properly abstracting from reality and dealing with failures in the domain they occurred in I propose failure handling strategies, a failure taxonomy extensible through experience, and discuss the relationship between symbolic/discrete and subsymbolic/continuous systems in terms of robot plans interacting with real world sensors and actuators. I concentrate on the domain of human-scale robot activities, specifically on doing household chores. Tasks in this domain offer many repeating patterns and are ideal candidates for abstracting, encapsulating, and modularizing robot plans into a more general form. This way, very similar plan structures are transformed into parameters that change the behavior of the robot while performing the task, making the plans more flexible. While performing tasks, robots encounter the same or similar situations over and over again. Albeit humans are able to benefit from this and improve at what they do, robots in general lack this ability. This thesis presents techniques for collecting and making robot experiences accessible to robots and outside observers alike, answering high level questions such as What are good spots to stand at for grasping objects from the fridge? or Which objects are especially difficult to grasp with two hands while they are in the oven? . By structuring and tapping into a robot's memory, it can make more informed decisions that are not based on manually encoded information, but self-improved behavior. To this end, I present several experience-based approaches to improve a robot's autonomous decisions, such as parameter choices, during execution time. Robots that interact with the real world are bound to deal with unexpected events and must properly react to failures of any kind of action. I present an extensible failure model that suits the structure of Generalized Plans and Episodic Memories and make clear how each module should deal with their own failures rather than directly handing them up to a governing cognitive architecture. In addition, I make a distinction between discrete parametrizations of Generalized Plans and continuous low level components, and how to translate between the two

An Enactivist Model of Improvisational Dance

An Enactivist Model of Improvisational Danc

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

The Impact of Batch Size on Worker Stress Perception

The current global competitiveness has led organizations to improve their processes, and Lean Production has been a responsive tool to cost reduction and efficiency improvement. Batch size plays an important role in production control, encompassing the introduction of Lean Production in several organizations. However, the application and sustainability of Lean Production have had their effectiveness contested. Several authors explain that the continuous search for improvement has created pressure among the workforce impacting their stress levels and well-being, causing issues in focus control, authority, moral disengagement, and others. This study aims to check the impact that Batch size has on the workforce stress perception. Using the NIOSH Generic Job-Stress Questionnaire (GJBQ), a Pilot Study was performed to check the reliability of the instrument. Subsequently, a Batch size Simulation using Lego Blocks to simulate a factory environment was performed with 50 participants and three trials with different Batch size s of 10, 5, and 1 respectively. A set of different roles were played by the participants, and that wasdivided into two categories (i) operators and (ii) Production supervisors. The GJSQ was applied at the end of each trial. Six factors were analyzed: (i) mental demands, (ii) quantitative workload, (iii) variance in workload, (iv) role conflict, (v) role ambiguity, and workload using Factors Analysis. Results indicate that the items are grouped differently from those proposed by NIOSH, indicating the existence of a new factor – Cognitive Demand. Results also indicated that the perception of stress increased while the Batch size decreased.Furthermore, males tend to have higher stress scores than females. The operational staff tends to present higher levels of stress whereas when moving from a Batch size of 10 to 1, the Production supervisors staff stress levels reduced. Responsibility for People increased in all trials, and within the roles, Variance in Workload increased only for the operators, and Quantitative Workload only for administrative roles. On the other hand, Cognitive Demands, and Mental Demand was reduced