Refine
Departments, institutes and facilities
Document Type
- Conference Object (56)
- Article (8)
- Preprint (3)
- Part of a Book (1)
- Doctoral Thesis (1)
Year of publication
Language
- English (69)
Keywords
- Robotics (5)
- Machine Learning (2)
- knowledge learning (2)
- neural networks (2)
- virtual reality (2)
- visualization (2)
- Agent-oriented software engineering (1)
- Artificial Intelligence (1)
- Assistive robots (1)
- Autonomous Systems (1)
As robots are becoming ubiquitous and more capable, the need for introducing solid robot software development methods is pressing to increase robots' task spectrum. This thesis is concerned with improving software engineering of robot perception systems. The presented research employs a model-based approach to provide the means to represent knowledge about robotics software. The thesis is divided into three parts, namely research on the specification, deployment and adaptation of robot perception systems.
RoCKIn@Work was focused on benchmarks in the domain of industrial robots. Both task and functionality benchmarks were derived from real world applications. All of them were part of a bigger user story painting the picture of a scaled down real world factory scenario. Elements used to build the testbed were chosen from common materials in modern manufacturing environments. Networked devices, machines controllable through a central software component, were also part of the testbed and introduced a dynamic component to the task benchmarks. Strict guidelines on data logging were imposed on participating teams to ensure gathered data could be automatically evaluated. This also had the positive effect that teams were made aware of the importance of data logging, not only during a competition but also during research as useful utility in their own laboratory. Tasks and functionality benchmarks are explained in detail, starting with their use case in industry, further detailing their execution and providing information on scoring and ranking mechanisms for the specific benchmark.
We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot. We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation and robust object recognition.