Architecture and Task Plan Co-Adaptation with Metaplan for Unmanned Underwater Vehicles

Unmanned Underwater Vehicles (UUVs) operate in complex environments and need to be able to adapt to sudden failures, or changes in the environment. To achieve autonomous operation, UUVs must have the ability to self-adapt in such cases. To effectively handle component failures and unexpected events, self-adaptation must be applied to both the...

Task-specific object grasps using primitive shapes and symbolic reasoning

In household and retail store environments, humans efficiently identify grasp regions of an object to perform everyday tasks. To enable robots to understand the requirements of a task and the properties of an object, a novel grasp framework, called the Shape Primitive and Reasoning Grasping Engine (SPaRGE), is proposed in this thesis. SPaRGE...

Iris - A knowledge Graph-based chatbot for Explaining Robotic Scenario Information to Human Operators in a Retail Setting

The problem of assisting users in comprehending the robotic scenario information in a retail setting has been studied. To design the system, an integrated ontology composed of several IEEE standard ontologies and a labelled property graph (LPG)-based ontology modified from the Web Ontology Language (OWL)-based ontology was proposed to symbolize...

Knowledge-based Approach for Mobile Manipulation with Active Inference

Achieving human-like action planning requires profound reasoning and context-awareness capabilities. It is especially true for autonomous robotic mobile manipulation in dynamic environments. In the case of component failure, the autonomous robotic system requires reliable adaptation capabilities combined with a consistent understanding of the...

Using Retinanet to determine local graspability for a suction actuator

Suction based robotic actuators have potential for the bin-picking industry, but are currently not usable due the needed speed, accuracy and ability to handle novel and adversarial objects. An evaluation of the state of the art grasp pipeline developed by Mahler et al. [1] for detecting grasps on novel objects lead us to split the problem of...

Zero-shot learning in pick-and-place tasks using neuro-symbolic concept learning

Pick and place systems that operate in a warehouse setting have been studied a lot recently due to the high economic value for e-commerce companies. In this thesis, the focus is on the perception pipeline that performs object recognition given a certain input data stream (typically RGB-D images). Impressive results regarding object recognition...

Symbolic reasoning about unseen objects from multimodal sensory feedback for manipulation

Robotically manipulating objects can be very challenging when not all of the environment can be fully observed, e.g. in environments which are physically and visually accessible from only a single side. By using multimodal sensory feedback and symbolic reasoning, conclusions can be drawn about the presence of objects that cannot be observed...

Situation-Aware Self-Adaptive Localisation Framework: A Knowledge Representation and Reasoning approach

Substantial efforts are being made to make robots more reliable and safe to work around humans. Robots often perform flawless demos in a controlled environment under the supervision of an operator but tend to fail in the real world when deployed for a long period of time due to faults and environmental disturbances. A robotic system is composed...

Robot manipulator control under the Active Inference framework

The Active Inference framework is a neuroscience theory based on the Free Energy Principle by Karl Friston that has gained considerable prominence as a general theory to explain action and perception. Despite its promising future and use in different fields, its applicability for robot control has been barely investigated in the literature so...