Author name cluster

Barbara Frank

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

IROS Conference 2016 Conference Paper

Automatic channel selection in neural microprobes: A combinatorial multi-armed bandit approach

Camilo Gordillo
Barbara Frank
István Ulbert
Oliver Paul
Patrick Ruther
Wolfram Burgard

State-of-the-art neural microprobes contain hundreds of electrodes within a single shaft. Due to hardware and wiring restrictions, it is usually only possible to measure a small subset of the available electrodes simultaneously. The selection of the best channels is typically performed offline either manually or automatically. However, having a fixed selection for long-term observation does not allow the system to react to changes in the neural activity, and may therefore lead to the loss of important information. In this paper, we formulate the process of autonomously selecting the best subset of electrodes as a combinatorial multi-armed bandit problem with non-stationary rewards, thus allowing the probe to adapt its selection policies online. In order to minimize exploratory actions of the probe, we furthermore take advantage of the existing dependencies between neighboring channels. Our approach is an adaptation of the discounted upper confidence bounds (D-UCB) algorithm, and identifies the electrodes providing the largest amount of non-redundant information. To the best of our knowledge, this is the first online approach for the problem of electrode selection. In extensive experiments, we demonstrate that our solution is not only able to converge towards an average optimal selection policy, but it is also able to react to changes in the neural activity or to damages of the recording electrodes.

Details

ICRA Conference 2015 Conference Paper

3D-reconstruction of indoor environments from human activity

Barbara Frank
Michael Ruhnke
Maxim Tatarchenko
Wolfram Burgard

Observing human activities can reveal a lot about the structure of the environment, the objects contained therein and also their functionality. This knowledge, in turn, can be useful for robots interacting with humans or for robots performing mobile manipulation tasks. In this paper, we present an approach to infer the geometric and functional structure of the environment and the position of certain relevant objects in it from human activity. We observe this activity using a full-body motion capture system consisting of a set of inertial measurement units. This is a hard problem since our data suit provides odometry estimates only, which severely drift over time. Therefore, we regard the objects inferred from the activities as landmarks in a graph-based simultaneous localization and mapping problem, which we optimize to obtain accurate estimates about the poses of the objects and the trajectory of the human. In extensive experiments, we demonstrate the effectiveness of the proposed method for the reconstruction of 3D representations. The resulting models not only contain a geometric but also a functional description of the environment and naturally provide a segmentation into individual objects.

Details

ICRA Conference 2015 Conference Paper

An autonomous robotic assistant for drinking

Sebastian Schroer
Ingo Killmann
Barbara Frank
Martin Voelker
Lukas Dominique Josef Fiederer
Tonio Ball
Wolfram Burgard

Stroke and neurodegenerative diseases, among a range of other neurologic disorders, can cause chronic paralysis. Patients suffering from paralysis may remain unable to achieve even basic everyday tasks such as liquid intake. Currently, there is a great interest in developing robotic assistants controlled via brain-machine interfaces (BMIs) to restore the ability to perform such tasks. This paper describes an autonomous robotic assistant for liquid intake. The components of the system include autonomous online detection both of the cup to be grasped and of the mouth of the user. It plans motions of the robot arm under the constraints that the cup stays upright while moving towards the mouth and that the cup stays in direct contact with the user's mouth while the robot tilts it during the drinking phase. To achieve this, our system also includes a technique for online estimation of the location of the user's mouth even under partial occlusions by the cup or robot arm. We tested our system in a real environment and in a shared-control setting using frequency-specific modulations recorded by electroencephalography (EEG) from the brain of the user. Our experiments demonstrate that our BMI-controlled robotic system enables a reliable liquid intake. We believe that our approach can easily be extended to other useful tasks including food intake and object manipulation.

Details

IROS Conference 2014 Conference Paper

Automatic channel selection and neural signal estimation across channels of neural probes

Olga Vysotska
Barbara Frank
István Ulbert
Oliver Paul
Patrick Ruther
Cyrill Stachniss
Wolfram Burgard

High-resolution microprobes are used to record single neuron activity in the brain. This technology is envisaged to be a central component for brain-controlled computers and robots. Current neural probes, however, allow for recording only a small number of the densely spaced electrodes simultaneously. Therefore, we address the problem of autonomously choosing, for a given number, the subset of electrodes with the corresponding size so as to extract as much information as possible. We first present an approach for predicting neural spikes across different channels of the probe. Our method employs nonparametric sparse Gaussian process regression to predict the signal of a channel given the signals recorded at neighboring sites. Second, we utilize the signal predictions for efficiently seeking for the subset of electrodes that minimizes the overall prediction error. In experiments carried out using real neural data, we demonstrate that our selection procedure provides highly accurate results. Furthermore, the solutions found in our experiments are close to the optimal solution.

Details

IROS Conference 2011 Conference Paper

Efficient motion planning for manipulation robots in environments with deformable objects

Barbara Frank
Cyrill Stachniss
Nichola Abdo
Wolfram Burgard

The ability to plan their own motions and to reliably execute them is an important precondition for autonomous robots. In this paper, we consider the problem of planning the motion of a mobile manipulation robot in the presence of deformable objects. Our approach combines probabilistic roadmap planning with a physical deformation simulation system. Since the physical deformation simulation is computationally demanding, we use efficient Gaussian process regression to estimate the deformation cost for individual objects based on training examples. We generate the training data by employing a simulation system in a preprocessing step. Consequently, no simulations are needed during runtime. We implemented and tested our approach on a mobile manipulation robot. Our experiments show that the robot is able to accurately predict and thus consider the deformation cost its manipulator introduces to the environment during motion planning. Simultaneously, the computation time is substantially reduced compared to a system that employs physical simulations online.

Details

IROS Conference 2010 Conference Paper

Learning the elasticity parameters of deformable objects with a manipulation robot

Barbara Frank
Ruediger Schmedding
Cyrill Stachniss
Matthias Teschner
Wolfram Burgard

In this paper, we consider the problem of determining the elasticity properties of deformable objects with a mobile manipulator equipped with a force sensorb. We learn the parameters by establishing a relation between the applied forces and the corresponding surface deformations. To determine the parameters, we minimize the difference between the observed surface of an object that is deformed by a real manipulator and the deformed surface obtained with a deformation simulator based on finite element methods. To establish the correspondences between the surfaces, our approach applies a 3D registration technique based on point-clouds which is used as the basis for comparing the results of the simulation system with the observations of the real deformations. As we demonstrate in real-world experiments, our system is able to estimate appropriate parameters that can be used to predict future deformations. This information can directly be incorporated into motion planning approaches that are designed for robots operating with deformable objects.

Details

ICRA Conference 2009 Conference Paper

Real-world robot navigation amongst deformable obstacles

Barbara Frank
Cyrill Stachniss
Ruediger Schmedding
Matthias Teschner
Wolfram Burgard

In this paper, we consider the problem of mobile robots navigating in environments with non-rigid objects. Whereas robots can plan their paths more effectively when they utilize the information about the deformability of objects, they also need to consider the influence of the interaction with the deformable objects on their measurements during the execution of their navigation task. In this paper, we present a probabilistic approach to identify the measurements influenced by the deformable objects. Based on a learned statistics about the influence of the deformable objects on the measurements, the robot is able to perform a sensor-based collision avoidance of unforeseen objects. We present experiments carried out with a real robot that illustrate the practicability of our approach.

Details

ICRA Conference 2008 Conference Paper

Efficient path planning for mobile robots in environments with deformable objects

Barbara Frank
Markus Becker 0003
Cyrill Stachniss
Wolfram Burgard
Matthias Teschner

The ability to reliably navigate through the environment is an important prerequisite for truly autonomous robots. In this paper, we consider the problem of path planning in environments with non-rigid obstacles such as curtains or plants. We present an approach that combines probabilistic roadmaps with a physical simulation of object deformations to determine a path that optimizes the trade-off between the deformation cost and the distance to be traveled. We describe how our approach utilizes Finite Element theory for calculating the deformation cost. Since the high computational requirements of the corresponding simulation prevent this method from being applicable online, we present an approximation that uses a preprocessing step to determine a deformation cost function for each object. This cost function allows us to estimate the deformation costs of arbitrary paths through the objects and is used to evaluate the trajectories generated by the roadmap planner online. We present experiments which demonstrate that the resulting algorithm plans nearly identical trajectories compared to the method that relies on computationally intense simulations. At the same time, our approach allows the robot to quickly calculate paths in environments with deformable objects.

Details