Stably grasping objects for a specific task is a hot research topic in robotics due to multiple degrees of freedom of hand kinematics, various shapes of objects, and incomplete visual sensing of objects (partial point clouds). This paper proposes an effective grasp planning method by integrating the crucial grasp cues (positions and orientations of thumb fingertips and the wrist) from humans' grasp experience. This approach has multiple advantages: greatly reducing the search space of the hand kinematics; no reconstruction or registration; being able to directly perform on the partial point cloud of objects. Meanwhile, for various shapes of objects which are partially observable in the single-view visual sensing, the presented approach learns the ``thumb'' grasp point employing a signature of histograms of orientations shape descriptor based on objects' category level. This method recognizes the grasp point according to the shape affordance at each point on the object, which performs the grasp point generalization on the familiar objects. Finally, we verify the developed methods via both simulations and experiments by grasping various shapes of objects.

While tactile sensing is becoming an indispensable robotic ability for object recognition and grasping manipulation, it still remains challenging for us to deal with the tactile data as the force distribution over the array sensors continuously changes as a function of time. In this paper, we present an efficient feature extractor named Linear Dynamic Systems-based Fuzzy C-means Method (LDS-FCM) to encode the tactile sequences, both spatially and temporally. To this end, we decompose every input sequence into multiple sub-sequences, each of which is locally described by a finite-ordered observability matrix of the LDS model. A fuzzy c-means method is then applied to cluster the local LDS descriptors for learning a codebook. Conditioned on the resulting codebook, the global tactile representation is formulated by employing two different frameworks to integrate the subsequences within each tactile sequence, namely, the Vector of Locally Aggregated Descriptor (VLAD) and Bag-of-Word (BoW) approaches. The effectiveness of the proposed model is verified by a variety of experimental evaluations on five benchmark datasets. It shows that our proposed method achieves a higher classification accuracy than the state-of-the-art models with a large margin.

Brain computer interface (BCI) is the only way for some special patients to communicate with the outside world and provide a direct control channel between brain and the external devices. As a non-invasive interface, the scalp electroencephalography (EEG) has a significant potential to be a major input signal for future BCI systems. Traditional methods only focus on a particular feature in the EEG signal, which limits the practical applications of EEG-based BCI. In this paper, we propose a algorithm for EEG classification with the ability to fuse multiple features. First, use the common spatial pattern (CSP) as the spatial feature and use wavelet coefficient as the spectral feature. Second, fuse these features with a fusion algorithm in orchestrate way to improve the accuracy of classification. Our algorithms are applied to the dataset IV a from BCI complete III. By analyzing the experimental results, it is possible to conclude that we can speculate that our algorithm perform better than traditional methods.