Abstract-We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases.
In REF , Bekiroglu et al. learned a probabilistic model of the relationships between grasp related features, using both successful and unsuccessful grasps.
Probabilistic consolidation of grasp experience
2016 IEEE International Conference on Robotics and Automation (ICRA)
2016 IEEE International Conference on Robotics and Automation (ICRA)
Computer Science Engineering