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Dissertationen

Robust Motion Generation for Mobile Manipulation — Integrating Control and Planning under Uncertainty

Arne Sieverling, 2018

This thesis contributes to algorithmic approaches for the motion generation problem for mobile manipulators. This problem is unsolved in unstructured environments, where the robot does not have access to precise models but must infer the state of the world with its sensors. The challenges for motion generation in these problems arise from the uncertainty prevalent in real world sensors, the different modalities that need to be considered, and real time constraints. Our approach in this thesis is to combine local feedback control with global planning under uncertainty to solve three different applications in manipulation.

mehr zu: Robust Motion Generation for Mobile Manipulation — Integrating Control and Planning under Uncertainty

Robot grasping by exploiting compliance and environmental constraints

Clemens Eppner, 2018

Greifen ist eine fundamentale Fähigkeit, die jedes autonome System beherrschen muss, welches die physikalische Welt verändern will. Die Komplexität des robotischen Greifens entspringt dem Umstand, dass jede Lösung eine Vielzahl an unterschiedlichen Komponenten enthält: den Handmechnismus, seine Regelung, die Wahrnehmung der Umwelt und Bewegungsplanung. Sie alle beeinflussen den Erfolg eines Griffs. Obwohl Greiflösungen in wohldefinierten Industrieanwendungen existieren, ist das generelle Greifproblem in unstrukturierten Umgebungen noch immer ungelöst.

Diese Dissertation stellt Greifplanungsalgorithmen vor, die zwei allgemeine Eigenschaften ausnutzen: die Nachgiebigkeit von Roboterhänden und die Einbeziehung der Umgebung eines Objekts. Wir vergleichen ihre Annahmen und diskutieren Limitierungen. Unsere Untersuchungen und Planungsalgorithmen zeigen, dass die Ausnutzung von Nachgiebigkeit in Händen und Festigkeit in der Umgebung zu erfolgreicherem Greifen führt.

mehr zu: Robot grasping by exploiting compliance and environmental constraints

Leveraging Novel Information for Coarse-Grained Prediction of Protein Motion

Ines Putz, 2018

Proteine sind an fast allen Funktionen in unseren Zellen beteiligt aufgrund ihrer Fähigkeit, Konformationsbewegungen mit chemischer Spezifität zu kombinieren. Informationen über die Bewegungen eines Proteins liefern somit Einblicke in seine Funktion. Proteine bewegen sich auf einer zerklüfteten Energielandschaft mit vielen lokalen Minima über ihrem hochdimensionalen Konformationsraum. Eine erschöpfende Abtastung dieses Raums übersteigt die verfügbaren Rechenressourcen für alle bis auf die kleinsten Proteine. Computergestützte Ansätze müssen daher die Energiefunktion und/oder die Auflösung des Modells vereinfachen aufgrund von Informationen darüber, was relevant ist und was ignoriert werden kann. Die Genauigkeit der Approximation hängt von der Genauigkeit der verwendeten Information ab. Informationen, die spezifisch für die Problemdomäne sind, d. h. Proteinbewegung in unserem Fall, führen normalerweise zu besseren Modellen.

In dieser Arbeit stelle ich ein neuartiges elastisches Netzwerkmodell von erlernten erhaltenen Kontakten, genannt lmcENM, vor. Es erweitert die Bewegungsreichweite, die durch diese Netz- werke erfasst werden können, durch das Ausnutzen neuer Informationen über die Struktur eines Proteins. Dies verbessert die allgemeine Anwendbarkeit von elastischen Netzwerkmodellen.

mehr zu: Leveraging Novel Information for Coarse-Grained Prediction of Protein Motion

Learning robotic perception through prior knowledge

Rico Jonschkowski, 2018

Intelligente Roboter müssen in der Lage sein zu lernen, um ihr Verhalten auf Basis von Erfahrung anzupassen. Um aus spezifischen Erfahrungen allgemeine Schlüsse zu ziehen, bedarf es jedoch Annahmen oder Vorwissen über die Welt.

Ich untersuche die Bedeutung dieses Vorwissens für das Lernen von Wahrnehmung. Obwohl Vorwissen eine zentrale Rolle im maschinellen Lernen spielt, ist es oft in den Details der Lernalgorithmen verborgen. Wenn wir dieses Vorwissen explizit machen, wird deutlich, dass aktuell benutztes Vorwissen die Welt aus der Sicht eines passiven ziellos Beobachters beschreibt. Solche allgemeinen KI-Annahmen sind hilfreich, weil sie auf Wahrnehmungsprobleme wie Bildklassifizierung anwendbar sind, bei denen es keinen Roboter gibt. Solche Annahmen sind auch für das Lernen robotischer Wahrnehmung hilfreich, aber sie übersehen einen wichtigen Aspekt des Problems: den Roboter.

mehr zu: Learning robotic perception through prior knowledge

Leveraging problem structure in interactive perception for robot manipulation of constrained mechanisms

Roberto Martín-Martín, 2018

In this thesis we study robot perception to support a specific type of manipulation task in unstructured environments, the mechanical manipulation of kinematic degrees of freedom. We propose a general approach for interactive perception and instantiations of this approach into perceptual systems to build kinematic, geometric and dynamic models of articulated objects.

mehr zu: Leveraging problem structure in interactive perception for robot manipulation of constrained mechanisms

On Decomposability in Robot Reinforcement Learning

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.

mehr zu: On Decomposability in Robot Reinforcement Learning

Soft Hands For Compliant Grasping

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".

mehr zu: Soft Hands For Compliant Grasping

Leveraging Novel Information Sources for Protein Structure Prediction

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.

mehr zu: Leveraging Novel Information Sources for Protein Structure Prediction

Multimodal human computer interaction in virtual realities based on an exoskeleton

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.

mehr zu: Multimodal human computer interaction in virtual realities based on an exoskeleton

Efficient Motion Planning for Intuitive Task Execution in Modular Manipulation Systems

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.

mehr zu: Efficient Motion Planning for Intuitive Task Execution in Modular Manipulation Systems

Interactive Perception of Articulated Objects for Autonomous Manipulation

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.

mehr zu: Interactive Perception of Articulated Objects for Autonomous Manipulation

Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction

TJ Brunette, 2011

The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima.

mehr zu: Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction

Exploiting Structure: A Guided Approach to Sampling-Based Robot Motion Planning

Brendan Burns, 2007

Robots already impact the way we understand our world and live our lives. However, their impact and use is limited by the skills they possess. Currently deployed autonomous robots lack the manipulation skills possessed by humans. To achieve general autonomy and applicability in the real world, robots must possess such skills.

mehr zu: Exploiting Structure: A Guided Approach to Sampling-Based Robot Motion Planning

Diplom- und Masterarbeiten

Entropy as an Organizing Principle for Selection in Evolutionary Robotics

Julius Faber, October 2016

In evolutionary computation, a goal-based objective function is typically unable to include the local challenges on the way towards its fulfillment and tends to cause the search to converge prematurely. Therefore, this work proposes to use objectives that are defined by different aspects of an individuals interaction with the environment and a selection procedure able to reallocate search efforts in order to avoid convergence. The objectives, curiosity, novelty and evolvability, differ in the time-scale they operate over and the amount of information they include about the problem structure. The common theme of these objectives is their tendency to increase the diversity of behaviors, which is assumed can act as general-purpose utility value. mehr zu: Entropy as an Organizing Principle for Selection in Evolutionary Robotics

Coordinated Actuation of Wrist and Handin Human Grasping with Robot Hands

Armin Schröder, November 2017

Classical robotic grasping approaches employ static behaviors: First the hand is maneuvered to the object, then the fingers are closed, and finally the hand is retracted from the scene with the grasped object. On the other hand, humans execute wrist movements concurrently with the fingers closure. They also demonstrate higher performance in terms of stability. I therefore hypothesize that this coordination of wrist and hand would enable robust robotic grasping.To evaluate this hypothesis, I conducted an experiment with seven human subjects grasping a set of seven objects using a robotic hand. The subjects guided the robotic hand with a handle and closed the fingers at will. I used a compliant robotic arm to limit the subjects’ control of wrist movement to different extents in five experimental conditions.

mehr zu: Coordinated Actuation of Wrist and Handin Human Grasping with Robot Hands

Identifying near-native multi-fragment sequence alignments in protein structure prediction

Fabian Salomon, May 2012

The introduction of protein fragment libraries in the mid-1990s meant a huge leap forward for protein structure prediction. With them, one was able to combine the best matching parts from a “scrapyard” of protein parts instead of focusing on just one template protein. Up until today, commonly used fragment libraries only contain relatively small, independent fragments. Consequently, these libraries can only model the (sequentially) local context, but can’t model structurally conserved regions that are sequentially discontiguous. We therefore developed a library of so called ”building blocks”. A building block is a set of structurally contiguous, sequentially discontiguous fragments found in two or more proteins.

mehr zu: Identifying near-native multi-fragment sequence alignments in protein structure prediction

Experimental Validation of Contact Dynamics for Prehensile Pushing

Roman Kolbert, January 2017

Most models in contact dynamics show some unrealistic behavior due to assumptions that were made for the sake of computational convenience. Unfortunately there is a lack of experimental work to validate these assumptions and to evaluate how realistic these contact modeling approaches are, which is the purpose of this thesis.

mehr zu: Experimental Validation of Contact Dynamics for Prehensile Pushing

Comparing Heuristics and Planners for Solving Simulated Lockboxes

Philipp Braunhart, July 2017

Many objects in the real world are articulated objects, e.g. office drawers or doors. These are objects made up of rigid bodies connected by joints. Robots are therefore often confronted with these articulated objects in the real world and should be able to successfully interact with them. mehr zu: Comparing Heuristics and Planners for Solving Simulated Lockboxes

Learning a Repetitive In-Hand Manipulation Task on the Sensorized RBO Hand 2 Using Policy Search

Marcel Radke, June 2019

The RBO Hand 2 is a highly compliant soft robotic hand. Its actuators passively adapt their shape to different objects and the environment. Even though the control of the pneumatic hand is relatively simple, it is capable of complex in-hand manipulation. The recent addition of liquid metal strain sensors has created the opportunity to obtain better feedback about the current state of the hand. In this thesis, we pick the task of "wiggling" a pen between the fingers and show that data from the strain sensors can be used to classify the position of the pen within the hand. Using a open-loop correction movement, the pen position can be adjusted before it drops from the hand. We also showed the possibility of closed-loop control using the strain sensor measurements as the current state and mass-flow commands of the pneumatic actuators as actions. mehr zu: Learning a Repetitive In-Hand Manipulation Task on the Sensorized RBO Hand 2 Using Policy Search

Recurrent State Representation Learning with Robotic Priors

Marco Morik, November 2018

Learning an internal state based on the observation is an important task in robotics. The sensor inputs are mostly high dimensional and only a small subspace is important for the robot. Previous work presented an unsupervised method training a neural net with a loss composed of robotic priors which has been effective in a markovian observation space. In this thesis, we try to extend this method to work on non markovian observation spaces, and train a recurrent network which should transfer the non markovian observations into a internal state space which fulfill the markov property. For this, we adapt the robotic prios to the new task and evaluate our method in a new experimental setting. As a goal, the robot should be able to solve a simple navigation task using only the learned state representation.

mehr zu: Recurrent State Representation Learning with Robotic Priors

Decoy-Based Template Retrieval for Comparative Modeling

Stefan Junghans, May 2017

In this thesis, we present an approach for retrieving templates that is inde- pendent of sequence similarity. In order to do this, we combine ab initio with comparative modeling by looking for similarities in ab initio decoys to identify good templates.

mehr zu: Decoy-Based Template Retrieval for Comparative Modeling

Predicting protein contacts by combining information from sequence and physicochemistry

Kolja Stahl, February 2016

In this thesis we present a new contact predictor that combines evolutionary, sequence-based and physicochemical information. The contact predictor uses a new and refined feature set with drastically reduced dimensionality.

mehr zu: Predicting protein contacts by combining information from sequence and physicochemistry

Force-Controlled Action Primitives for Interactive Perception

Georg Flick, Oct 2015

Interactive Perception exploits the robot capabilities to interact with the environment to reveal hidden properties, like the kinematic structures of articulated objects. However, when the robot faces a new environment, it needs to decide on how to interact to maximize the information gain based on sensor data, and use compliant controllers that allow the articulation to guide the motion...

mehr zu: Force-Controlled Action Primitives for Interactive Perception

Increasing the Stifness of a Pneumatic Actuator with Granular and Layer Jamming

Vincent Wall, May 2014

The ability to selectively stiffen otherwise compliant soft actuators increases their versatility and dexterity. The thesis investigates granular jamming and layer jamming as two possible methods to achieve stiffening with PneuFlex actuators, a type of soft continuum actuator. It details five designs of jamming compartments that can be attached to an actuator. The strength of the most effective prototype based on layer jamming is also validated in the context of pushing buttons.

mehr zu: Increasing the Stifness of a Pneumatic Actuator with Granular and Layer Jamming

Searching for objects through location reasoning in a probabilistic, relational world

Malte Lorbach, April 2014

In this thesis, we present a novel probabilistic representation for object relationships, and apply it to object search.

mehr zu: Searching for objects through location reasoning in a probabilistic, relational world

Motion Planning in Dynamic Environments with Probabilistic ConnectivityRoadmaps

Peter Lehner

We present an incremental method for motion generation in environments with unpredictably moving and initially unknown obstacles. The key to the method is its incremental nature: it locally augments and adapts global motion plans in response to changes in the environment, even if they significantly change the connectivity of the world.

mehr zu: Motion Planning in Dynamic Environments with Probabilistic ConnectivityRoadmaps

Shifting the Boundary Between Planning and Control - Task-Consistent Motion Generation in Unstructured Environments

Nicolas Kuhnen, October 2012

In this thesis we present a motion generation approach that shifts the boundary between planning and control methods to generate task-consistent motion under uncertainty.

mehr zu: Shifting the Boundary Between Planning and Control - Task-Consistent Motion Generation in Unstructured Environments

Using recurring spatially contiguous substructures in the Protein Database for protein structure prediction

Mahmoud Mabrouk, 2012

In this thesis, we use sequentially non-continuous, but structurally contiguous, structural motifs in protein structure prediction

mehr zu: Using recurring spatially contiguous substructures in the Protein Database for protein structure prediction

Aligning a Sequence to Non-contiguous Sequence Fragments

Stefan Dörr

Sequence alignment methods are frequently used in protein structure prediction to identify homologous protein structures. The existing methods make local and global alignments between sequentially contiguous protein sequences. However, in our ongoing protein structure prediction research, we have a unique sequence to sequence alignment problem. The potential sequence alignments need to be made between a target sequence and the sets of sequence fragments, where the sequence fragments may not be sequentially contiguous.

mehr zu: Aligning a Sequence to Non-contiguous Sequence Fragments

Grasping using Visual Feedback

Georg Bartels

Interactively explore and grasp real-world objects using visual feedback.

mehr zu: Grasping using Visual Feedback

3D Perception for Grasping

Stefan Schrandt

Allow Robots to grasp unknown objects while perceive the environment and the objects with a 3D sensors.

mehr zu: 3D Perception for Grasping

Using tree-based robot motion planning algorithms for protein loop closure

Florian Kamm

A novel approach for the protein loop closure problem inspired from robot modeling is developed using the kinematic chain representation of the loop chain and a motion planning technique.

mehr zu: Using tree-based robot motion planning algorithms for protein loop closure

Simulating Physical Dynamics of Virtual Objects with a Wearable Haptic Interface

Lars Raschendörfer

Haptic devices enhance the range of multi-modal interaction in virtual reality environments. With the wearable haptic device, developed at the RBO Lab, this interaction is not limited to a small workspace any longer. The wider range of motion allows for new application scenarios.

mehr zu: Simulating Physical Dynamics of Virtual Objects with a Wearable Haptic Interface

Evaluation of Three Sensor Technologies for Use in Soft Robot Fingers (Kopie 1)

Stefan Schirmeister, März 2016.

Sensing for soft continuum actuators as a necessary technology has emerged recently with the development of so called soft hands, which exploit the high deformability of soft structures and materials. Unfortunately, soft, stretchable sensors capable of withstanding a stretch of 100% are commercially not available. At the same time their tight integration into actuators is required to address the specific challenges of continuously deforming actuators. The thesis evaluates three potential sensor technologies for their suitability in soft hands. The thesis investigates their robustness, ease of use, long term stability and responsiveness with respect to the intended application in soft hands.

mehr zu: Evaluation of Three Sensor Technologies for Use in Soft Robot Fingers (Kopie 1)

Bachelorarbeiten

Effects of model complexity on speed and accuracy of visual servoing for manipulation

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Georg Hieronimus, November 2015

To perform fine manipulation tasks without error, it is necessary for the robot to position his fingers with very high accuracy and precision. Using a sophisticated model, a recursive pose estimation by a particle filter based on visual data can provide this, but may fail to provide the necessary speed. My work therefore focuses on estimating the influence of different model parameters to the accuracy and speed of the pose estimation. The most important parameter is the number of particles, which increases the accuracy strongly up to a number of 500. In addition, the computational cost scales linear with the number of particles. Different techniques using the GPU to reduce the time per particle are presented and evaluated.

mehr zu: Effects of model complexity on speed and accuracy of visual servoing for manipulation

EET-Based Motion Planning Applied to Protein-Ligand Interactions

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Friederike Fischer, April 2017

For drug design it is essential to know which ligands can reach the active site of a protein. These ligands are potential candidates that inhibit or activate the given protein, and thereby cure a disease. We will show how to use sampling-based motion planning to solve protein-ligand disassembly problems. mehr zu: EET-Based Motion Planning Applied to Protein-Ligand Interactions

Identification of Beneficial Morphological Computation on Soft Hands

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Marlon Kupfer, March 2019

Compliance in soft hands can be both beneficial and detrimental to functionality. Although recent work has shown the benefits of compliance to object and environment geometry, there is little work in identifying and avoiding the negative aspects of compliance while controlling soft hands. However, a planner or a feedback-controller that exploits compliance should avoid the regions of detrimental morphological computation and guide the interactions to the favorable ones. Luckily recent work in simulation has shown promising results in differentiating between beneficial/detrimental morphological computations. The challenge ahead is to whether these results can be transferred to real systems. Our lab's work in hand sensorization is a possible tool in this path. mehr zu: Identification of Beneficial Morphological Computation on Soft Hands

Characterizing PneuFlex Actuator Deformations Using Liquid Metal Strain Sensors

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Gabriel Zöller, August 2016

Deformations of soft actuators contain valuable information about their interactions with the environment. This thesis develops a methodical approach to enable limited shape sensing of the PneuFlex actuator used for the RBO Hand 2. mehr zu: Characterizing PneuFlex Actuator Deformations Using Liquid Metal Strain Sensors

Position-Based Servoing via Probabilistic Part-Based Object Models

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Manuel Wöllhaf, January 2015

To extend the field of application of robots in unstructured environments it is necessary to develop new techniques of environment perception and interpretation. These methods must give machines the capability to generate sufficient information, which enables them to fulfill their tasks with the aid of their sensors. Therefore it is required to extract local and task dependent invariant structure out of the unstructured environment. mehr zu: Position-Based Servoing via Probabilistic Part-Based Object Models

Three dimensional Joint Detection

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Andreas Orthey, September 2012

Enable a robot to infer the type of joint between moving clusters of 3d features. This can further be used to build a kinematic structure of an observed object. mehr zu: Three dimensional Joint Detection

Extended Visual Servoing for Manipulation

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Sebastian Koch, February 2011

The interest in robots that are able to act in unstructured environments is increasing. mehr zu: Extended Visual Servoing for Manipulation

A Practical Guide to Transformed Predictive State Representations

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Niklas Gebauer, September 2015

Predictive state representations (PSRs) are gaining a lot of attention in the robotics community lately because, in theory, they promise a powerful model that might be learned directly from data. But the practical application of PSRs remains a difficult procedure. In this practical guide we aim to ease and encourage practical work with PSRs. mehr zu: A Practical Guide to Transformed Predictive State Representations

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