Ph.D Theses
Ines Putz, 2018
Proteins are involved in almost all functions in our cells due to their ability to combine
conformational motion with chemical specificity. Hence, information about the motions of a
protein provides insights into its function. Proteins move on a rugged energy landscape with
many local minima, which is imposed on their high-dimensional conformational space. Exhaustive
sampling of this space exceeds the available computational resources for all but the smallest
proteins. Computational approaches thus have to simplify the potential energy function and/or
resolution of the model using information about what is relevant and what can be ignored. The
accuracy of the approximation depends on the accuracy of the used information. Information
that is specific to the problem domain, i.e. protein motion in our case, usually results in better
models.
In this thesis, I propose a novel elastic network model of learned maintained contacts, lmcENM.
It expands the range of motions that can be captured by such simplified models by leveraging
novel information about a protein’s structure. This improves the general applicability of elastic
network models.
more to: Leveraging Novel Information for Coarse-Grained Prediction of Protein Motion
Rico Jonschkowski, 2018
Intelligent robots must be able to learn; they must be able to adapt their behavior based on experience. But generalization from past experience is only possible based on assumptions or prior knowledge (priors for short) about how the world works.
I study the role of these priors for learning perception. Although priors play a central role in machine learning, they are often hidden in the details of learning algorithms. By making these priors explicit, we can see that currently used priors describe the world from the perspective of a passive disinterested observer. Such generic AI priors are useful because they apply to perception scenarios where there is no robot, such as image classification. These priors are still useful for learning robotic perception, but they miss an important aspect of the problem: the robot.
more to: Learning robotic perception through prior knowledge
Sebastian Höfer, 2017
Reinforcement learning is a computational framework that enables machines to learn from trial-and-error interaction with the environment. In recent years, reinforcement learning has been successfully applied to a wide variety of problem domains, including robotics. However, the success of the reinforcement learning applications in robotics relies on a variety of assumptions, such as the availability of large amounts of training data, highly accurate models of the robot and the environment as well as prior knowledge about the task.
In this thesis, we study several of these assumptions and investigate how to generalize them. To that end, we look at these assumptions from different angles. On the one hand, we study them in two concrete applications of reinforcement learning in robotics: ball catching and learning to manipulate articulated objects. On the other hand, we develop an abstract explanatory framework that relates the assumptions to the decomposability of problems and solutions. Taken together, the concrete case studies and the abstract explanatory framework enable us to make suggestions on how to relax the previously stated assumptions and how to design more effective solutions to robot reinforcement learning problems.
more to: On Decomposability in Robot Reinforcement Learning
Raphael Deimel, 2017
Raphael Deimel's thesis reconsiders hand design from the perspective of providing first and foremost robust and reliable grasping, instead of precise control of posture and simple mechanical modelabilty. This results in a fundamentally different manipulator hardware, so called soft hands, that are made out of rubber and fibers which make them highly adaptable. His thesis covers not only hand designs, but also provides an elaborate collection of methods to design, simulate and rapidly prototype soft robots, referred to as the "PneuFlex toolkit".
more to: Soft Hands For Compliant Grasping
Michael Bohlke-Schneider, 2015
Three-dimensional protein structures are an invaluable stepping stone towards the understanding of cellular processes. Not surprisingly, state-of-the-art structure prediction methods heavily rely on information. This thesis aims to leverage new information sources: Physicochemical information encoded in predicted structure models and experimental data from high-density cross-linking / mass spectrometry (CLMS) experiments. We demonstrate that these information sources allow improved structure prediction and the reconstruction of human serum albumin domain structures from experimental data collected in its native environment, human blood serum.
more to: Leveraging Novel Information Sources for Protein Structure Prediction
Ingo Kossyk, 2012
The key features of this system are a high degree of immersion into the computer generated virtual environment and a large working volume. The high degree of immersion will be achieved by multimodal human-exoskeleton interaction based on haptic effects, audio and three- dimensional visualization. The large working volume will be achieved by a lightweight wearable construction that can be carried on the back of the user.
more to: Multimodal human computer interaction in virtual realities based on an exoskeleton
Markus Rickert, Mai 2011
Computationally efficient motion planning mus avoid exhaustive exploration of high-dimensional configuration spaces by leveraging the structure present in real-world planning problems. We argue that this can be accomplished most effectively by carefully balancing exploration and exploitation.
Exploration seeks to understand configuration space, irrespective of the planning problem, and exploitation acts to solve the problem, given the available information obtained by exploration. We present an exploring/exploiting tree (EET) planner that balances its exploration and exploitation behavior.
The planner acquires workspace information and subsequently uses this information for exploitation in configuration space. If exploitation fails in difficult regions the planner gradually shifts to its behavior towards exploration.
more to: Efficient Motion Planning for Intuitive Task Execution in Modular Manipulation Systems
Dov Katz, 2011
This thesis develops robotic skills for manipulating novel articulated objects. The degrees of freedom of an articulated object describe the relationship among its rigid bodies, and are often relevant to the object's intended function. Examples of everyday articulated objects include scissors, pliers, doors, door handles, books, and drawers. Autonomous manipulation of articulated objects is therefore a prerequisite for many robotic applications in our everyday environments.
more to: Interactive Perception of Articulated Objects for Autonomous Manipulation
