A Probabilistic Framework for Task-Oriented Grasp Stability Assessment
Paper i proceeding, 2013

We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.

Robot sensing systems


Stability analysis

Probabilistic logic

Bayes methods



Yasemin Bekiroglu

Chalmers, Signaler och system, System- och reglerteknik, Reglerteknik

Dan Song

Kungliga Tekniska Högskolan (KTH)

Lu Wang

Kungliga Tekniska Högskolan (KTH)

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

IEEE International Conference on Robotics and Automation
Karlsruhe, ,


Robotteknik och automation

Datavetenskap (datalogi)

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