Assessing grasp stability based on learning and haptic data

Yasemin Bekiroglu*, Janne Laaksonen, Jimmy Alison Jørgensen, Ville Kyrki, Danica Kragic

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

127 Citations (Scopus)


An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machine-learning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements from fingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research.

Original languageEnglish
Article number5759756
Pages (from-to)616-629
Number of pages14
JournalIEEE Transactions on Robotics
Issue number3
Publication statusPublished - Jun 2011
MoE publication typeA1 Journal article-refereed


  • Force and tactile sensing
  • grasping
  • learning and adaptive systems


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