Semantic Representation of Robot Manipulation with Knowledge Graph.

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information.

Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators.

Redundant manipulators are widely used in fields such as human-robot collaboration due to their good flexibility. To ensure efficiency and safety, the manipulator is required to avoid obstacles while tracking a desired trajectory in many tasks. Conventional methods for obstacle avoidance of redundant manipulators may encounter joint singularity or exceed joint position limits while tracking the desired trajectory.

High-Performance Anodic Vulcanization-Pretreated Gated P-π-M-N InAs/GaSb Superlattice Long-Wavelength Infrared Detector.

The InAs/GaSb superlattice infrared detector has been developed with tremendous effort. However, the performance of it, especially long-wavelength infrared detectors (LWIR), is still limited by the electrical performance and optical quantum efficiency (QE). Forcing the active region to be p-type through proper doping can highly improve QE, and the gating technique can be employed to greatly enhance electrical performance.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight.