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- Bekiroglu, Yasemin, et al.
(författare)
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A probabilistic framework for task-oriented grasp stability assessment
- 2013
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Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE Computer Society. - 1050-4729. - 9781467356411 ; , s. 3040-3047
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Bekiroglu, Yasemin, et al.
(författare)
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Assessing Grasp Stability Based on Learning and Haptic Data
- 2011
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Ingår i: IEEE Transactions on robotics. - : IEEE Robotics and Automation Society. - 1552-3098 .- 1941-0468. ; 27:3, s. 616-629
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Tidskriftsartikel (refereegranskat)abstract
- 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 machinelearning 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 fromfingertips; 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.
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| 7. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
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Grasp Stability from Vision and Touch
- 2012
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We study the exploitation of tactile and visual feedback to predict the stability of a grasp before attempting to lift an object and thus to prevent failures. Our robot learns an empirical representation of stable and unstable grasps by exploring given grasping configurations.
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| 8. |
- Bekiroglu, Yasemin, et al.
(författare)
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Integrating Grasp Planning with Online Stability Assessment using Tactile Sensing
- 2011
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Ingår i: IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. - 9781612843865 ; , s. 4750-4755
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Konferensbidrag (refereegranskat)abstract
- This paper presents an integration of grasp planning and online grasp stability assessment based on tactile data. We show how the uncertainty in grasp execution posterior to grasp planning can be dealt with using tactile sensing and machine learning techniques. The majority of the state-of-the-art grasp planners demonstrate impressive results in simulation. However, these results are mostly based on perfect scene/object knowledge allowing for analytical measures to be employed. It is questionable how well these measures can be used in realistic scenarios where the information about the object and robot hand may be incomplete and/or uncertain. Thus, tactile and force-torque sensory information is necessary for successful online grasp stability assessment. We show how a grasp planner can be integrated with a probabilistic technique for grasp stability assessment in order to improve the hypotheses about suitable grasps on different types of objects. Experimental evaluation with a three-fingered robot hand equipped with tactile array sensors shows the feasibility and strength of the integrated approach.
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| 9. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
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Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment
- 2011
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper studies the viability of concurrent object pose tracking and tactile sensing for assessing grasp stability on a physical robotic platform. We present a kernel logistic regression model of pose- and touch-conditional grasp success probability. Models are trained on grasp data which consist of (1) the pose of the gripper relative to the object, (2) a tactile description of the contacts between the object and the fully-closed gripper, and (3) a binary description of grasp feasibility, which indicates whether the grasp can be used to rigidly control the object. The data is collected by executing grasps demonstrated by a human on a robotic platform composed of an industrial arm, a three-finger gripper equipped with tactile sensing arrays, and a vision-based object pose tracking system. The robot is able to track the pose of an object while it is grasping it, and it can acquire grasp tactile imprints via pressure sensor arrays mounted on its gripper’s fingers. We consider models defined on several subspaces of our input data – using tactile perceptions or gripper poses only. Models are optimized and evaluated with f-fold cross-validation. Our preliminary results show that stability assessments based on both tactile and pose data can provide better rates than assessments based on tactile data alone.
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