Analytic Grasp Success Prediction with Tactile Feedback
Paper in proceeding, 2016

Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

Training data

Tactile sensors

Predictive models




Computational modeling


Robert Krug

Örebro University

Achim Lilienthal

Örebro University

Danica Kragic

Royal Institute of Technology (KTH)

Yasemin Bekiroglu

University of Birmingham

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

978-146738026-3 (ISBN)

2016 IEEE International Conference on Robotics and Automation
Stockholm, Sweden,

Subject Categories


Computer Science



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