Publications

2017

Felix Burget, Lukas Fiederer, Daniel Kuhner, Martin Völker, Johannes Aldinger, Robin Schirrmeister, Chau Do, Joschka Boedecker,
Bernhard Nebel, Tonio Ball and Wolfram Burgard
Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills
In: Proceedings of the European Conference on Mobile Robotics (ECMR), 2017.
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As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception. In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.
```
@inproceedings{burget2017ecmr, 
author={Felix Burget, Lukas Fiederer, Daniel Kuhner, Martin Völker, Johannes Aldinger, Robin Schirrmeister,
 Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball and Wolfram Burgard}, 
title={Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills},
booktitle={European Conference on Mobile Robotics (ECMR), 2017},
pages={},
year={2017},
organization={IEEE}
}
```

2016

Felix Burget, Maren Bennewitz and Wolfram Burgard
BI^2RRT*: An Efficient Sampling-Based Path Planning Framework for Task-Constrained Mobile Manipulation
In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2016.
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Mobile manipulators installed in warehouses and factories for conveying goods between working stations need to meet the requirements of time-critical workflows. More- over, the systems are expected to deal with changing tasks, cluttered environments and constraints imposed by the goods to be delivered. In this paper, we present a novel planning framework for generating asymptotically optimal paths for mobile manipulators subject to task constraints. Our approach introduces the Bidirectional Informed RRT* (BI^2RRT*) that extends the Informed RRT* towards bidirectional search and satisfaction of end-effector task constraints. In various experiments, we demonstrate the efficiency of BI^2RRT* for both unconstrained and constrained mobile manipulation planning problems. As the results show, our planning framework finds better solutions than Informed RRT* and Bidirectional RRT* in less planning time.
```
@inproceedings{burget2016mobilemanipulation, 
author={Burget, Felix and Burgard, Wolfram and Bennewitz, Maren}, 
title={BI^2RRT*: An Efficient Sampling-Based Path Planning Framework for Task-Constrained Mobile Manipulation},
booktitle={Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on},
pages={},
year={2016},
organization={IEEE}
}
```

2015

Felix Burget, Christoph Maurer, Wolfram Burgard and Maren Bennewitz
Learning Motor Control Parameters for Motion Strategy Analysis of Parkinson’s Disease Patients
In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2015.
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Although the neurological impairments of Parkin- son’s disease (PD) patients are well known to go along with motor control deficits, e.g., tremor, rigidity, and reduced move- ment, not much is known about the motor control parameters affected by the disease. In this paper, we therefore present a novel approach to human motions analysis using motor control strategies with joint weight parameterization. We record the motions of healthy subjects and PD patients performing a hand coordination task with the whole-body XSens MVN motion capture system. For our motion strategy analysis we then follow a two step approach. First, we perform a complexity reduction by mapping the recorded human motions to a simplified kinematic model of the upper body. Second, we reproduce the recorded motions using a Jacobian weighted damped least squares controller with adaptive joint weights. We developed a method to iteratively learn the joint weights of the controller with the mapped human joint trajectories as reference input. Finally, we use the learned joint weights for a quantitative comparison between the motion control strategies of healthy subjects and PD patients. Other than expected from clinical experience, we found that the joint weights are almost evenly distributed along the arm in the PD group. In contrast to that, the proximal joint weights of the healthy subjects are notably larger than the distal ones.
```
@inproceedings{burget2015learning, 
author={Burget, Felix and Maurer, Christoph and Burgard, Wolfram and Bennewitz, Maren}, 
title={Learning motor control parameters for motion strategy analysis of Parkinson's disease patients},
booktitle={Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on},
pages={5019--5025},
year={2015},
organization={IEEE}
}
```

Felix Burget and Maren Bennewitz
Stance Selection for Humanoid Grasping Tasks by Inverse Reachability Maps
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2015.
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In grasping tasks carried out with humanoids, knowledge about the robot’s reachable workspace is important. Without this knowledge, it might be necessary to repeatedly adapt the stance location and call an inverse kinematics solver before a valid robot configuration to reach a given grasping pose can be found. In this paper, we present an approach to select an optimal stance location in SE(2) for a humanoid robot’s feet relative to a desired grasp pose. We use a precomputed representation of the robot’s reachable workspace that stores quality information in addition to spatial data. By inverting this representation we obtain a so-called inverse reachability map (IRM) containing a collection of potential stance poses for the robot. The generated IRM can subsequently be used to select a statically stable, collision-free stance configuration to reach a given grasping target. We evaluated our approach with a Nao humanoid in simulation and in experiments with the real robot. As the experiments show, using our approach optimal stance poses can easily be obtained. Furthermore, the IRM leads to a substantially increased success rate of reaching grasping poses compared to other meaningful foot placements within the vicinity of the desired grasp.
```
@INPROCEEDINGS{burgetf15icra,
author = {Felix Burget and Maren Bennewitz},
title = {Stance Selection for Humanoid Grasping Tasks by Inverse Reachability Maps},
booktitle = {Proc.~of the IEEE International Conference on Robotics and
Automation (ICRA)},
year = 2015,
month = {May},
address = {Seattle, USA}
}
```

2014

Jonas Koenemann, Felix Burget, and Maren Bennewitz
Real-time Imitation of Human Whole-Body Motions by Humanoids
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014.
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In this paper, we present a system that enables humanoid robots to imitate complex whole-body motions of humans in real time. In our approach, we use a compact human model and consider the positions of the endeffectors as well as the center of mass as the most important aspects to imitate. Our system actively balances the center of mass over the support polygon to avoid falls of the robot, which would occur when using direct imitation. For every point in time, our approach generates a statically stable pose. Hereby, we do not constrain the configurations to be in double support. Instead, we allow for changes of the support mode according to the motions to imitate. To achieve safe imitation, we use retargeting of the robot’s feet if necessary and find statically stable configurations by inverse kinematics. We present experiments using human data captured with an Xsens MVN motion capture system. The results show that a Nao humanoid is able to reliably imitate complex whole-body motions in real time, which also include extended periods of time in single support mode, in which the robot has to balance on one foot.
```
@INPROCEEDINGS{koenemann14icra,
author = {Jonas Koenemann, Felix Burget, and Maren Bennewitz},
title = {Real-time Imitation of Human Whole-Body Motions by Humanoids },
booktitle = {Proc.~of the IEEE International Conference on Robotics and Automation (ICRA)},
year = 2014,
month = {May},
address = {Hong Kong, China}
}
```

F. Burget, M. Cenciarini, B. Meier, H. Bast, M. Bennewitz, W. Burgard, and C. Maurer
Towards a Closed-Loop System for Real-Time Calibration of Neural Stimulation Parameters
using Motion Data
In: Proceedings of the ICRA Workshop on Wearable Robotics for Motion Assistance and Rehabilitation - RoboAssist, 2014.
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The vision behind our project is a closed-loop system for continuous deep brain stimulation (DBS) based on features extracted from complex motion data. Our focus is on Parkinsons Disease (PD) patients, with a possible expansion to related neurological disorders. The system we envision is lightweight, will continuously gather motion information, will automatically extract high-level features from this data in real-time, and will feed back this information to continuously calibrate the parameters of a deep-brain stimulation (DBS) electrode.
```
@INPROCEEDINGS{burgetf14icra,
author = {F. Burget, M. Cenciarini, B. Meier, H. Bast, M. Bennewitz, W. Burgard, and C. Maurer},
title = {Towards a Closed-Loop System for Real-Time Calibration of Neural Stimulation Parameters using Motion Data},
booktitle = {Proc.~of the IEEE International Conference on Robotics and Automation (ICRA)}, year = 2014, month = {May},
address = {Hong Kong, China},
}
```

2013

Felix Burget, Armin Hornung, and Maren Bennewitz
Whole-Body Motion Planning for Manipulation of Articulated Objects
In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2013.
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Humanoid service robots performing complex object manipulation tasks need to plan whole-body motions that satisfy a variety of constraints: The robot must keep its balance, self-collisions and collisions with obstacles in the environment must be avoided and, if applicable, the trajectory of the end-effector must follow the constrained motion of a manipulated object in Cartesian space. These constraints and the high number of degrees of freedom make whole-body motion planning for humanoids a challenging problem. In this paper, we present an approach to whole-body motion planning with a focus on the manipulation of articulated objects such as doors and drawers. Our approach is based on rapidly-exploring random trees in combination with inverse kinematics and considers all required constraints during the search. Models of articulated objects hereby generate hand poses for sampled configurations along the trajectory of the object handle. We thoroughly evaluated our planning system and present experiments with a Nao humanoid opening a drawer, a door, and picking up an object. The experiments demonstrate the ability of our framework to generate solutions to complex planning problems and furthermore show that these plans can be reliably executed even on a low-cost humanoid platform.
```
@INPROCEEDINGS{burget13icra,
author = {Felix Burget and Armin Hornung and Maren Bennewitz},
title = {Whole-Body Motion Planning for Manipulation of Articulated Objects},
booktitle = {Proc.~of the IEEE International Conference on Robotics and Automation (ICRA)},
year = 2013,
month = {May},
address = {Karlsruhe, Germany}
}
```

2012

Felix Burget.
Whole-Body Motion Planning for Robotic Manipulation of Articulated Objects.
Master-Thesis, Università di Roma - La Sapienza, 2012.
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In this thesis, we present a sampling-based approach to generate whole-body motions for a humanoid robot. In particular, the planner developed is capable to generate valid whole-body motions for body repositioning as well as for grasping and manipulation of articulated objects, such as doors or drawers. Besides the generic constraints involved in motion planning, such as joint limits and collision avoidance, motions of a humanoid robot are required to satisfy stability and closure constraints. Additionally manipulation constraints arise, once the robot has grasped the handle of an object to be manipulated. In the work at hand, our planner, which is an extended variant of the RRT-CONNECT algorithm, will be presented. In the experiments we will show that the whole-body motion planner allows to perform tasks that are not feasible by a decomposition approach that plans motions for the lower and upper body consecutively. Furthermore, examples of manipulation planning will be presented and the planner is thoroughly evaluated. The experiments have been carried out successfully on both, the simulated and real humanoid robot platform.
```
 @MASTERSTHESIS{burget12masterthesis,
author = {Felix Burget},
title = {Whole-Body Motion Planning for Robotic Manipulation of 
Articulated Objects},
school = {Università di Roma - La Sapienza},
year = {2012},
type = {Master-Thesis},
address = {Rome, Italy},
month = {Dezember}
}
```

Felix Burget

Publications

2017

2016

2015

2014

2013

2012