Author name cluster

Felix Burget

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.

6 papers

1 author row

IROS Conference 2018 Conference Paper

Closed-Loop Robot Task Planning Based on Referring Expressions

Daniel Kuhner
Johannes Aldinger
Felix Burget
Moritz Göbelbecker
Wolfram Burgard
Bernhard Nebel

Increasing the accessibility of autonomous robots also for inexperienced users requires user-friendly and high-level control opportunities of robotic systems. While automated planning is able to decompose a complex task into a sequence of steps which reaches an intended goal, it is difficult to formulate such a goal without knowing the internals of the planning system and the exact capabilities of the robot. This becomes even more important in dynamic environments in which manipulable objects are subject to change. In this paper, we present an adaptive control interface which allows users to specify goals based on an internal world model by incrementally building referring expressions to the objects in the world. We consider fetch-and-carry tasks and automatically deduce potential high-level goals from the world model to make them available to the user. Based on its perceptions our system can react to changes in the environment by adapting the goal formulation within the domain-independent planning system.

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IROS Conference 2016 Conference Paper

BI 2 RRT*: An efficient sampling-based path planning framework for task-constrained mobile manipulation

Felix Burget
Maren Bennewitz
Wolfram Burgard

Mobile manipulators installed in warehouses and factories for conveying goods between working stations need to meet the requirements of time-critical workflows. Moreover, 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 2 RRT*) that extends the Informed RRT* [1] towards bidirectional search and satisfaction of end-effector task constraints. In various experiments, we demonstrate the efficiency of BI 2 RRT* 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.

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IROS Conference 2015 Conference Paper

Learning motor control parameters for motion strategy analysis of Parkinson's disease patients

Felix Burget
Christoph Maurer
Wolfram Burgard
Maren Bennewitz

Although the neurological impairments of Parkinson's disease (PD) patients are well known to go along with motor control deficits, e. g. , tremor, rigidity, and reduced movement, 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.

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ICRA Conference 2015 Conference Paper

Stance selection for humanoid grasping tasks by inverse reachability maps

Felix Burget
Maren Bennewitz

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.

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ICRA Conference 2014 Conference Paper

Real-time imitation of human whole-body motions by humanoids

Jonas Koenemann
Felix Burget
Maren Bennewitz

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.

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ICRA Conference 2013 Conference Paper

Whole-body motion planning for manipulation of articulated objects

Felix Burget
Armin Hornung
Maren Bennewitz

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.

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