In practice, there are many imbalanced data classification problems, for example, spam filtering, credit card fraud detection and software defect prediction etc. it is important in theory as well as in application for investigating the problem of imbalanced data classification. In order to deal with this problem, based on extreme learning machine autoencoder, this paper proposed an approach for addressing the problem of binary imbalanced data classification. The proposed method includes 3 steps. (1) the positive instances are used as seeds, new samples are generated for increasing the number of positive instances by extreme learning machine autoencoder, the generated new samples are similar with the positive instances but not same. (2) step (1) is repeated several times, and a balanced data set is obtained. (3) a classifier is trained with the balanced data set and used to classify unseen samples. The experimental results demonstrate that the proposed approach is feasible and effective.

In this paper, we address the issue robots developing a reaching ability, as human infants do. Specifically, a novel infant-inspired framework is proposed based on recent findings that the emergence of reaching is the product of a deeply embodied process. The methodology behind our framework is to develop proprioception, fixation, and internal models (including both the forward and inverse models) in three sequential phases, with the whole developing process being driven by self-produced motor babbling. In the first phase, an autoencoder-based proprioception model is proposed. In the second phase, a simplified strategy for imitating the function of fixation is developed. In the third phase, a new forward model and two new inverse models are further proposed. We evaluated our framework and associated models with the PKU-HR6.0II physical robot and two simulated robots. Experiments confirm that the PKU-HR6.0II could successfully develop its reaching ability in a manner similar to that of human infants through our proposed framework. Better performance and adaptability are also demonstrated in comparison to existing benchmarks. It is also shown that the proposed framework has the potential to achieve other manipulation abilities such as grasping and placing.

Arm motion control is fundamental for robot accomplishing complicated manipulation tasks. Different movements can be organized by configuring a series of motion units. Our work aims at equipping the robot with the ability to carry out Basic unit movements (BUMs), which are used to constitute various motion sequences so that the robot can drive its hand to a desired position. With the definition of BUMs, we explore a learning approach for the robot to develop such an ability by leveraging deep learning technique. In order to generate the BUM regarding to the current arm state, an internal inverse model is developed. We propose to use Conditional generative adversarial networks (CGANs) to establish the inverse model to generate the BUMs. The experimental results on a humanoid robot PKU-HR6.0II illustrate that CGANs could successfully generate multiple solutions given a BUM, and these BUMs can be used to constitute further reaching movement effectively.

The concept of direction is critical for a robot to establish its abilities in spatial perception. This paper addresses the issue of how a robot developmentally forms the concept of direction. We propose a novel framework, based on the related mechanisms of humans, in which motion cues are employed instead of other commonly used sensing means like vision or audition. Using motion cues is actually one of the most important ways for humans to acquire concepts. This leads to advantages in two ways: models based on motion cues are usually more convenient for a robot’s control of motion; multi-modal perceptual cues can complement each other and result in improved robustness. The framework behind our methodology lies in developmentally forming the concept of direction in two successive phases. (i) The robot acquires the perceptual representation of its motion by motor babbling. (ii) The corresponding higher-level features are gradually captured, based on which the conceptual representation is obtained and then the concept of direction is formed. The proposed framework and the corresponding models are evaluated with a PKU-HR5.0II humanoid robot. The results show that the robot can successfully form the concept of direction in a manner similar to that of humans.