A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes

Vision-based grasping plays an important role in the robot providing better services. It is still challenging under disturbed scenes, where the target object cannot be directly grasped constrained by the interferences from other objects. In this article, a robotic grasping approach with firstly moving the interference objects is proposed based on elliptical cone-based potential fields. Single-shot multibox detector (SSD) is adopted to detect objects, and considering the scene complexity, Euclidean cluster is also employed to obtain the objects without being trained by SSD. And then, we acquire the vertical projection of the point cloud for each object. Considering that different objects have different shapes with respective orientation, the vertical projection is executed along its major axis acquired by the principal component analysis. On this basis, the minimum projected envelope rectangle of each object is obtained. To construct continuous potential field functions, an elliptical-based functional representation is introduced due to the better matching degree of the ellipse with the envelope rectangle among continuous closed convex curves. Guided by the design principles, including continuity, same-eccentricity equivalence, and monotonicity, the potential fields based on elliptical cone are designed. The current interference object to be grasped generates an attractive field, whereas other objects correspond to repulsive ones, and their resultant field is used to solve the best placement of the current interference object. The effectiveness of the proposed approach is verified by experiments.

Developing Emotion-Aware Human–Robot Dialogues for Domain-Specific and Goal-Oriented Tasks

Developing dialogue services for robots has been promoted nowadays for providing natural human–robot interactions to enhance user experiences. In this study, we adopted a service-oriented framework to develop emotion-aware dialogues for service robots. Considering the importance of the contexts and contents of dialogues in delivering robot services, our framework employed deep learning methods to develop emotion classifiers and two types of dialogue models of dialogue services. In the first type of dialogue service, the robot works as a consultant, able to provide domain-specific knowledge to users. We trained different neural models for mapping questions and answering sentences, tracking the human emotion during the human–robot dialogue, and using the emotion information to decide the responses. In the second type of dialogue service, the robot continuously asks the user questions related to a task with a specific goal, tracks the user’s intention through the interactions and provides suggestions accordingly. A series of experiments and performance comparisons were conducted to evaluate the major components of the presented framework and the results showed the promise of our approach.