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Ph.D Theses

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.

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

Robot grasping by exploiting compliance and environmental constraints

Clemens Eppner, 2018

Grasping is a crucial skill for any autonomous system that needs to alter the physical world. The complexity of robot grasping stems from the fact that any solution comprises various components: Hand design, control, perception, and planning all affect the success of a grasp. Apart from picking solutions in well-defined industrial scenarios, general grasping in unstructured environment is still an open problem.

In this thesis, we exploit two general properties to devise grasp planning algorithms: the compliance of robot hands and the stiffness of the environment that surrounds an object. The investigations and planning algorithms show that exploiting compliance in hands and stiffness in the environment leads to improved grasp performance.

more to: Robot grasping by exploiting compliance and environmental constraints

Leveraging Novel Information for Coarse-Grained Prediction of Protein Motion

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

Learning robotic perception through prior knowledge

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

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.

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

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.

more to: Leveraging Novel Information Sources for Protein Structure Prediction

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".

more to: 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.

more to: 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.

more to: 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.

more to: 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.

more to: 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.

more to: 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.

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

Diploma / Master Theses

Teleoperation of a Soft Robotic Hand with a Dataglove

Patrick Ehinger, March 2020

Robot hands are one of the most important but also most complex parts of a robot system. In the field of soft robotics, one goal is to design robot hands that resemble the human hand and can adapt their capabilities. Especially important is the ability to manipulate objects in the hand, the so-called in-hand manipulation. But to carry out in-hand manipulation complex movements are required. In order to be able to execute these movements, we would like to teleoperate the RBO-Hand 3 with a data glove. The aim of this thesis is to remotely control a soft pneumatically operated robot hand developed by the department with the help of a data glove in order to be able to carry out in-hand manipulations and to perform experiments on in-hand manipulation with the RBO-Hand 3. more to: Teleoperation of a Soft Robotic Hand with a Dataglove

Active Acoustic Sensing

Gabriel Zoeller, January 2020

Sensors are an important part of any robot control system. While soft pneumatic actuators can't use most sensors from rigid robotics, they exhibit properties that make new sensing modalities possible. The air inside the air chamber of a pneumatic actuator conducts sound and this sound carries information about where it originated and which path it traveled.

more to: Active Acoustic Sensing

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. more to: 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.

more to: Recurrent State Representation Learning with Robotic Priors

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.

more to: Coordinated Actuation of Wrist and Handin Human Grasping with Robot Hands

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.

more to: Comparing Heuristics and Planners for Solving Simulated Lockboxes

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.

more to: Decoy-Based Template Retrieval for Comparative Modeling

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.

more to: Experimental Validation of Contact Dynamics for Prehensile Pushing

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. more to: Entropy as an Organizing Principle for Selection in Evolutionary Robotics

Evaluation of Three Sensor Technologies for Use in Soft Robot Fingers

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.

more to: Evaluation of Three Sensor Technologies for Use in Soft Robot Fingers

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.

more to: 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...

more to: Force-Controlled Action Primitives for Interactive Perception

Increasing the Stiffness 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.

more to: Increasing the Stiffness 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.

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

Motion planning in dynamic environments with probabilistic connectivity roadmaps

Peter Lehner, 2014

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.

more to: Motion planning in dynamic environments with probabilistic connectivity roadmaps

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.

more to: Shifting the Boundary between Planning and Control - Task-Consistent Motion Generation in Unstructured Environments

Aligning a Sequence to Non-contiguous Sequence Fragments

Stefan Dörr, 2012

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.

more to: Aligning a Sequence to Non-contiguous Sequence Fragments

Grasping using Visual Feedback

Georg Bartels, 2012

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

more to: Grasping using Visual Feedback

3D Perception for Grasping

Stefan Schrandt, 2012

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

more to: 3D Perception for Grasping

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

Simulating Physical Dynamics of Virtual Objects with a Wearable Haptic Interface

Lars Raschendörfer, 2011

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.

more to: Simulating Physical Dynamics of Virtual Objects with a Wearable Haptic Interface

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

Florian Kamm, 2010

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.

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

Bachelor Theses

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.

more to: Identification of Beneficial Morphological Computation on Soft Hands

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.

more to: EET-Based Motion Planning Applied to Protein-Ligand Interactions

Characterizing PneuFlex Actuator Deformations Using Liquid Metal Strain Sensors

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Finding a sensor layout for the PneuFlex actuator

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. more to: Characterizing PneuFlex Actuator Deformations Using Liquid Metal Strain Sensors

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.

more to: Effects of model complexity on speed and accuracy of 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. more to: A Practical Guide to Transformed Predictive State Representations

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 fulfil 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. more to: 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. more to: 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. more to: Extended Visual Servoing for Manipulation

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