We present our second generation tactile sensor for the Shadow Dexterous Hand's palm. We were able to significantly improve the tactile sensor characteristics by utilizing our latest barometer-based tactile sensing technology with linear (R2≥ 0.9996) sensor output and no noticeable hysteresis. The sensitivity threshold of the tactile cells and the spatial density were both dramatically increased. We demonstrate the benefits of the new sensor by re-running an experiment to estimate the stiffness of different objects that we originally used to test our first generation palm sensor. The results underline a considerable performance boost in estimation accuracy, just due to the improved tactile skin. We also propose a revised neural network architecture that even further improves the average classification accuracy to 96% in a 5-fold cross-validation.

We present a novel force-limitation algorithm to protect fragile robot actuation and transmission systems (like tendon-driven systems) from early wear-out. Inspired by human muscle fatigue, we model artificial actuator fatigue by integrating applied forces over time, gradually limiting applicable forces when fatigue increases. The algorithm is applied to protect from long-term tendon wear-out on our Shadow Dexterous Hands as well as to restrict grasping forces for compliant grasping in unknown environments. In grasping experiments the efficiency of various grasp-force limitation approaches are compared to each other, including one exploiting tactile-based slip detection.

The advent of advanced tactile sensing technology triggered the development of methods to employ them for grasp evaluation, online slip detection, and tactile servoing. In contrast to recent approaches to slip detection, distinguishing slip from non-slip conditions, we consider the more difficult task of distinguishing different types of slippage. Particularly we consider an object pushing task, where forces can only be applied from the top. In that case, the robot needs to notice when the object successfully moves vs. when the object gets stuck while the finger slips over its surface. As an example, consider the task of pushing around a piece of paper. We propose and evaluate three different convolutional network architectures and proof the applicability of the method for online classification in a robot pushing task.

Population based encodings allow to represent probabilistic and fuzzy state estimates. Such a representation will be introduced and applied for the case of a redundant manipulator. Following the Mean of Multiple Computations principle, a neural network model (PbMMC) is presented in which the overall complexity is divided into multiple local relationships. This allows to solve inverse, forward and mixed kinematic problems. The local transformations in between the kinematic variables can be sufficiently well learned by small single MLP layers. The population codes of the kinematic variables are based on nested periodic receptive fields which allow to express multiple weighted state estimates. Therefore, the model as such is quite flexible as it can keep track of multiple possible solutions at the same time.

We present a novel, soft, tactile skin composed of a fabric-based, stretchable sensor technology based on the piezoresistive effect. Softness is achieved by a combination of a soft silicone padding covered by a skin of more durable, tearproof silicone with an imprinted surface pattern mimicking human glabrous skin, found e.g. in fingertips. Its very thin layer structure (starting from 2.5 mm) facilitates integration on existing robot surfaces, particularly on small and highly curved links. For example, we augmented our Shadow Dexterous Hand with 12 palm sensors, and 2 resp. 3 sensors in the middle resp. proximal phalanges of each finger. To demonstrate the usefulness and efficiency of the proposed sensor skin, we performed a challenging classification task distinguishing squeezed objects based on their varying stiffness.

In this paper, we present a probabilistic spatial approach to build compact 3-D representations of unknown objects probed by tactile sensors. Our approach exploits the high frame rates provided by modern tactile sensors and utilizes Kalman filters to build a probabilistic model of the contact point cloud that is efficiently stored in a kd-tree. The quality of generated shape representations is compared with a naive averaging approach, and we show that our method provides superior accuracy. We also evaluate the feasibility of object classification combining the generated object representations, together with the iterative closest point algorithm.

In games of incomplete information, the analyst must specify the players' choices, payoff functions, and hierarchies of beliefs (about the payoffs of the game). The importance of correctly specifying players' choices or actual payoff functions is well understood. (See, Kreps-Wilson [8, 1982] and Milgrom-Roberts [10, 1982], amongst many others.)

Heifetz, Meier and Schipper (2005) introduced a generalized state-space model that allows for non-trivial unawareness among several individuals and strong properties of knowledge. We show that this generalized state-space model arises naturally if states consist of maximally consistent sets of formulas in an appropriate logical formulation.

We define a generalized state-space model with interactive unawareness and probabilistic beliefs. Such models are desirable for many potential applications of asymmetric unawareness. We develop Bayesian games with unawareness, define equilibrium, and prove existence. We show how equilibria are extended naturally from lower to higher awareness levels and restricted from higher to lower awareness levels. We use our unawareness belief structure to show that the common prior assumption is too weak to rule out speculative trade in all states. Yet, we prove a generalized "No-trade" theorem according to which there can not be common certainty of strict preference to trade. Moreover, we show a generalization of the "No-agreeing-to-disagree" theorem.

Standard state-space models, which are widely used in economics, preclude non-trivial forms of unawareness as shown by Dekel, Lipman and Rustichini (1998). We define a generalized state-space model that allows for unawareness. In order to facilitate applications we make no explicit use of modal syntax within the sematic model. Our model satisfies all "S4" properties as well as all desiderata on unawareness proposed by Modica and Rustichini (1999), Halpern (2001) as well as Dekel, Lipman and Rustichini (1998).