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.

The adoption of robots for service tasks in natural environments calls for the use of sensors to allow manipulation of objects under imperfect environment knowledge and the use of knowledge transfer from humans. This paper addresses these challenges by proposing a new abstraction architecture for embodiment independent sensor-based control of manipulation. The aim is to address three specific challenges: hardware independent control of manipulation, use of sensors to alleviate problems of complexity and uncertainty of the environment, and ease of transferring knowledge over different embodiments through a hierarchical abstract representation of manipulation skills. The proposed abstraction architecture is demonstrated for hardware independence and failure detection on two different manipulator platforms.