Beetz, Michael
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Beetz, Michael
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Beetz, Michael
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Item-typ:Veröffentlichung, Longterm Generalized Actions for Smart, Autonomous Robot Agents(2018-02-05); ; ; 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.Dissertation381 178 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Task-adaptable, Pervasive Perception for Robots Performing Everyday Manipulation(2020-02-11); ; ; Intelligent robotic agents that help us in our day-to-day chores have been an aspiration of robotics researchers for decades. More than fifty years since the creation of the first intelligent mobile robotic agent, robots are still struggling to perform seemingly simple tasks, such as setting or cleaning a table. One of the reasons for this is that the unstructured environments these robots are expected to work in impose demanding requirements on a robota s perception system. Depending on the manipulation task the robot is required to execute, different parts of the environment need to be examined, the objects in it found and functional parts of these identified. This is a challenging task, since the visual appearance of the objects and the variety of scenes they are found in are large. This thesis proposes to treat robotic visual perception for everyday manipulation tasks as an open question-asnswering problem. To this end RoboSherlock, a framework for creating task-adaptable, pervasive perception systems is presented. Using the framework, robot perception is addressed from a systema s perspective and contributions to the state-of-the-art are proposed that introduce several enhancements which scale robot perception toward the needs of human-level manipulation. The contributions of the thesis center around task-adaptability and pervasiveness of perception systems. A perception task-language and a language interpreter that generates task-relevant perception plans is proposed. The task-language and task-interpreter leverage the power of knowledge representation and knowledge-based reasoning in order to enhance the question-answering capabilities of the system. Pervasiveness, a seamless integration of past, present and future percepts, is achieved through three main contributions: a novel way for recording, replaying and inspecting perceptual episodic memories, a new perception component that enables pervasive operation and maintains an object belief state and a novel prospection component that enables robots to relive their past experiences and anticipate possible future scenarios. The contributions are validated through several real world robotic experiments that demonstrate how the proposed system enhances robot perception.Dissertation1297 494 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Perception for imagination-enabled robots(2025-10-14); ; ; Recent advancements in robotics and computer vision have enhanced object recognition and control strategies. However, these developments do not fully tackle the challenges of autonomous manipulation in dynamic, unstructured environments like households. Current systems often rely on specialized algorithms for perception, which lack generalizability and fail to verify the plausibility of their results. This thesis proposes a comprehensive framework that enhances robotic perception and manipulation in dynamic, unstructured environments by integrating a photorealistic, physics-enabled game engine. The core contributions of this research are threefold. First, it presents a unified perception architecture that combines imagistic reasoning, process-level control, and perception task adaptation within a single system. This architecture enables robots to construct internal hypotheses, simulate expected sensor data, and verify perceptual results against rendered scenes, facilitating grounded and introspective perception in real-world tasks. Second, the thesis presents a game-engine-based belief representation, utilizing real-time simulation as an internal model of belief states to enable high-fidelity visual hypothesis generation. The simulated environment represents a dynamic world model, including the robot state, allowing the system to assess the plausibility of perceptual results and predict the visual consequences of actions. Lastly, Perception Pipeline Trees (PPTs) are introduced as a modular process model for adaptive perception execution. PPTs combine hierarchical execution with flexible control flow, supporting reactive switching, concurrent processing, and introspective verification. This model accommodates conventional vision tasks and imagistic reasoning processes within a unified representation. The framework demonstrates effectiveness in real-world applications, including household assistance scenarios where robots perform tasks such as recognizing and manipulating objects, as well as tracking and interacting with humans. By enabling robots to not only observe but also reason about their environment through simulation, this work advances task adaptability, perception accuracy, and reasoning capability, laying the foundation for the next generation of intelligent, imagination-enabled robots.Dissertation75 60