Object-centered hybrid reasoning for whole-body mobile manipulation

Leidner, Daniel; Dietrich, Alexander; Schmidt, Florian; Borst, Christoph; Albu-Schäffer, Alin

doi:10.1109/icra.2014.6907099

Cited by 46 publications

(33 citation statements)

References 27 publications

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“…Various works have used this robot-centric capability map to select the robot base position by overlapping the capability map with end-effector goal poses [19], [20], [21]. Leidner et al also addresses regions of interest and integration with a whole-body controller (doing whole-body inverse-kinematics) once the task is in progress [21]. Vahrenkamp et al inverts the capability map from [18] to create a grasp-centric scoring method.…”

Section: Related Workmentioning

confidence: 99%

“…The capability map is the discretized 3-D workspace around a robot arm, scored by the number of discretized orientations the end effector can reach at each discretized 3-D point [18]. Various works have used this robot-centric capability map to select the robot base position by overlapping the capability map with end-effector goal poses [19], [20], [21]. Leidner et al also addresses regions of interest and integration with a whole-body controller (doing whole-body inverse-kinematics) once the task is in progress [21].…”

Section: Related Workmentioning

confidence: 99%

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Task-centric selection of robot and environment initial configurations for assistive tasks

Kapusta

Park

Kemp

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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When a mobile manipulator functions as an assistive device, the robot's initial configuration and the configuration of the environment can impact the robot's ability to provide effective assistance. Selecting initial configurations for assistive tasks can be challenging due to the high number of degrees of freedom of the robot, the environment, and the person, as well as the complexity of the task. In addition, rapid selection of initial conditions can be important, so that the system will be responsive to the user and will not require the user to wait a long time while the robot makes a decision. To address these challenges, we present Task-centric initial Configuration Selection (TCS), which unlike previous work uses a measure of task-centric manipulability to accommodate state estimation error, considers various environmental degrees of freedom, and can find a set of configurations from which a robot can perform a task. TCS performs substantial offline computation, so that it can rapidly provide solutions at run time. At run time, the system performs an optimization over candidate initial configurations using a utility function that can include factors such as movement costs for the robot's mobile base. To evaluate TCS, we created models of 11 activities of daily living (ADLs) and evaluated TCS's performance with these 11 assistive tasks in a computer simulation of a PR2, a robotic bed, and a model of a human body. TCS performed as well or better than a baseline algorithm in all of our tests against state estimation error.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Task-centric selection of robot and environment initial configurations for assistive tasks

Kapusta

Park

Kemp

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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show abstract

“…Please refer to [22] for a more detailed description. This approach has been successfully evaluated to solve everyday manipulation tasks [22], mobile manipulation tasks [24], and force-sensitive whole-body manipulation [25].…”

Section: A Providing Informationmentioning

confidence: 99%

A knowledge-driven shared autonomy human-robot interface for tablet computers

Birkenkampf

Leidner

Borst

2014

2014 IEEE-RAS International Conference on Humanoid Robots

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Autonomous and teleoperated robots have been proven to be capable of solving complex manipulation tasks. However, autonomous robots can only be deployed in predefined domains and teleoperation requires full attention of a human operator. Therefore, combining autonomous capabilities of the robot with teleoperation is desirable, yet balancing the work load between robot and operator for intuitive human-robot interfaces is still an open issue. In this paper, we present a knowledge-driven tablet computer application for commanding a robot on a high level of abstraction. The application guides an operators decisions based on the actual world state of the robot and enables the operator to command object-centered actions, which are autonomously interpreted symbolically and geometrically by the robot. We evaluate our approach using the humanoid robot Rollin' Justin for an elaborate manipulation experiment and an user study. We show that our approach efficiently balances the work load between robot and operator and provides an intuitive interface for human-robot interaction.

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“…Though the reachability map (RM) was originally designed for fixed-base robots, research has directly extended RM to mobile systems by randomly or systematically selecting different base positions [7], [8]. Vahrenkamp et al [9] introduced the inverse reachability map (IRM) by encoding the reachability information in the end-effector frame rather than in the fixed base frame, which allows floating-base robots to automatically find appropriate stance locations and reaching configurations given a desired end-effector pose.…”

Section: Introductionmentioning

confidence: 99%

Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps

Yang

Merkt

Ferrolho

et al. 2017

IEEE Robot. Autom. Lett.

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Abstract-A key prerequisite for planning manipulation with locomotion of humanoid robots in complex environments is to find a valid end-pose with a stable stance location and a collisionfree, balanced full-body configuration. Prior work based on the Inverse Reachability Map assumed that the feet are placed next to each other around the stance location on a horizontal plane. Additionally, the success rate was correlated with the coverage density of the sampled space, which is in turn limited by the memory needed for storing the map.We present a Paired Forward-Inverse Dynamic Reachability Maps that extends the inverse Dynamic Reachability Map (iDRM) by integrating it with forward reachability maps according to the inherent kinematic structure of the robot. By exploiting the combinatorics of this modularity, greater coverage in each map can be achieved while keeping a low number of stored samples. This enables us to draw samples from a much richer dataset to effectively plan end-poses for single-handed as well as bimanual tasks on uneven terrain. We demonstrated the method on the 38-DoF NASA Valkyrie humanoid utilizing the whole body to exploit redundancy for accomplishing manipulation tasks on uneven terrain while avoiding obstacles.

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Object-centered hybrid reasoning for whole-body mobile manipulation

Cited by 46 publications

References 27 publications

Task-centric selection of robot and environment initial configurations for assistive tasks

Task-centric selection of robot and environment initial configurations for assistive tasks

A knowledge-driven shared autonomy human-robot interface for tablet computers

Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps

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