Hochgeschwender, Nico

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Preferred name
Hochgeschwender, Nico
Official Name
Hochgeschwender, Nico
Email
nico.hochgeschwender@uni-bremen.de
ORCID

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    Item-typ:Veröffentlichung,
    Probabilistic action prospection based on experiences - representation, learning and reasoning in autonomous robotic agents
    (2024-05-07)
    Picklum, Mareike
    ;
    Hochgeschwender, Nico
    ;
    Beetz, Michael
    ;
    Beetz, Michael
    The concept of autonomous robotic companions assisting with tedious tasks or daily routines has long been a futuristic ambition, especially in scenarios deemed too complex for seamless operation. The multitude of variables, intricate interconnections, and numerous (side) effects on seemingly straightforward influences pose significant challenges for them to function competently without human intervention. However, if robots were equipped with knowledge about themselves, their surroundings, and the objects within it, they could address various queries about their environment and undertake tasks independently, drawing further insights from their own experiences and sensory inputs. For example, they could effortlessly respond to contextual inquiries such as “Where should I position myself in the kitchen to locate the milk carton?”, “What route should I take from my current location?” and “Is the refrigerator open or closed?”. Addressing uncertainty and its associated limitations is crucial for constructing a comprehensive world model that provides an autonomous agent with the necessary capabilities to operate independently – potentially leading to robots becoming valuable household aids and companions. This thesis presents BayRoB, a probabilistic framework integrating probabilistic hybrid action models to assist autonomous agents in making informed, context-driven decisions under uncertainty. BayRoB utilizes probabilistic models to represent an autonomous robot’s belief state and offers mechanisms to track changes in this state over time. The framework incorporates a novel formalism that enables the learning, representation of and reasoning over joint probability distributions representing action and object designators. Enabling robots to adeptly handle uncertain situations significantly enhances their decision-making abilities and contributes to their capacity to anticipate action outcomes and environmental changes, thereby promoting autonomy. The approach of integrating probabilistic hybrid models into a framework, as demonstrated by BayRoB, with learning occurring through experiential data, holds promise for fundamentally enhancing the decision-making processes of autonomous agents. A critical aspect of this advancement lies in the incorporation of joint probability distributions, encompassing both aspects of the world and the agent itself. This integration is essential for facilitating informed decision-making rooted in experiential knowledge. By incorporating probabilistic models to efficiently learn, represent, and reason across various aspects of the agent and its environment, it becomes feasible to equip autonomous robots with cognitive abilities. This empowerment enables them to accurately predict action outcomes based on context, furnishing the agent with essential tools to make well-informed decisions when selecting optimal actions and parameters for their tasks. The presented approach is the first ever to learn and use such comprehensive joint probabilities for a robotic system. Experiments showcase that BayRoB is capable of refining underspecified plans and allow reasoning over arbitrary matters of the agent, the available actions and their parameterizations as well as aspects of the agent’s environment. A browser-based web interface allows the user to investigate the system’s capabilities and reproduce the conducted experiments.
    Dissertation
      321  214
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    Item-typ:Veröffentlichung,
    Modular task modeling and creation of high-level building blocks for implementing complex robot applications
    (2025-06-24)
    Wirkus, Malte 
    ;
    Kirchner, Frank
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    Hochgeschwender, Nico
    ;
    Kirchner, Frank
    Component-based frameworks for the development of robot control software provide tools to support software development and define a common component interface for the software created in this framework. This has a significant impact on robot programming: while the tools increase developer productivity, the common component interface increases software reusability. As a result, roboticists can now benefit from an extensive collection of software components. In a component-based software framework, the robot's behavior is defined by the selection of individual components, their configuration and the connections between the components. The reuse of software components significantly reduces development work, particularly in the development of general algorithms. For robot systems that have to be able to handle a wide range of applications with numerous behaviors and algorithmic approaches, current software frameworks lack the flexibility to ensure the necessary variability in robot control, which hinders the realization of versatile robot systems. Model-driven software development enables the realization of development processes in which recurring development tasks are automated through model transformation pipelines. The use of domain-specific languages makes it possible to design complex software systems in a more efficient and accessible way. This work investigates methods for the model-driven development of robot controllers. The focus is on the specification and execution of modular robot applications that combine a variety of different control paradigms and algorithms and can be adapted to different robot systems or application scenarios. A software ecosystem is presented, consisting of (a) a software system for modeling and executing robot controllers that are freely reconfigurable and interchangeable at the robot's runtime, (b) a high-level task modeling and execution environment that allows the integration of multiple behavior description paradigms into a common framework, and (c) a meta-modeling environment for creating high-level integration building blocks that can be adapted to different robot systems or tasks. After an introduction to the general research area and the technological foundations, the individual proposed technologies are presented. The underlying model representations and domain-specific languages are explained and the implementation of the software systems based on them is presented. Each software system is evaluated and discussed individually, whereby practical validation tests with real robot systems, dedicated performance measurements and theoretical considerations regarding the limits and performance of the software are made The evaluation confirms the applicability of the software for the intended application area of reconfigurable robot controllers and for the integration of complex control systems. In addition, open issues are identified and extensions of the proposed software are discussed for possible future work.
    Dissertation
      43  38