Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands

Morales, Antonio; Asfour, Tamim; Azad, Pedram; Knoop, Steffen; Dillmann, Ruediger

doi:10.1109/iros.2006.282367

Cited by 83 publications

(57 citation statements)

References 16 publications

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“…Very powerful methods such as those based on probabilistic roadmap and its variants [25,26] use a C-space description of the environment and graph based methods for search. Another approach to the same problem has been adopted by using various control schemes in conjunction with offline grasp planners [21] or visual tracking systems [27]. A synergistic combination of grasp planning, visual tracking and arm trajectory generation is presented in [28].…”

Section: Manipulation Planningmentioning

confidence: 99%

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Shukla

Billard

2012

Robotics and Autonomous Systems

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Section: Manipulation Planningmentioning

confidence: 99%

Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

Shukla

Billard

2012

Robotics and Autonomous Systems

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“…Some researchers have argued that pure contactlevel approaches usually do not take the hand constraints into consideration (Wren and Fisher, 1995;Miller and Allen, 1999;Morales et al, 2006;, producing a set of contacts which are not reachable in practice. In fact, the lack of accurate sensors and errors in the models and robot-object positioning put difficulties to practical implementation.…”

Section: Graspingmentioning

confidence: 99%

“…This argument is supported by the small number of articles that apply the contact-level approach with real robotic hands under realistic conditions. An alternative approach is to grasp with predefined hand postures, which is called the knowledge-based approach to grasping (Stansfield, 1991;Okamura et al, 2000), or also the qualitative approach (Morales et al, 2006). These approaches consider a set of predefined prehensile patterns and classify them according to its suitability for the object geometry and for the task.…”

Section: Graspingmentioning

confidence: 99%

A framework for compliant physical interaction

2009

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Although the grasp-task interplay in our daily life is unquestionable, very little research has addressed this problem in robotics. In order to fill the gap between the grasp and the task, we adopt the most successful approaches to grasp and task specification, and extend them with additional elements that allow to define a grasp-task link. We propose a global sensor-based framework for the specification and robust control of physical interaction tasks, where the grasp and the task are jointly considered on the basis of the task frame formalism and the knowledge-based approach to grasping. A physical interaction task planner is also presented, based on the new concept of task-oriented hand preshapes. The planner focuses on manipulation of articulated parts in home environments, and is able to specify automatically all the elements of a physical interaction task required by the proposed framework. Finally, several applications are described, showing the versatility of the proposed approach, and its suitability for the fast implementation of robust physical interaction tasks in very different robotic systems.

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“…In [Mori andMalik, 2002, Mori andMalik, 2006], it is shown that shape contexts can be used for describing human silhouettes and thus allow the recognition of 3D human poses. A silhouette is represented by a set of shape contexts: one shape context for each contour point.…”

Section: Pose Estimation Based On Silhouettesmentioning

confidence: 99%

“…Our framework for grasp planning and execution, incorporating the object recognition and pose estimation systems developed in this thesis, is described in [Morales et al, 2006].…”

Section: Convenient Acquisition Of Training Viewsmentioning

confidence: 99%