Learning hybrid locomotion skills—Learn to exploit residual actions and modulate model-based gait control

This work has developed a hybrid framework that combines machine learning and control approaches for legged robots to achieve new capabilities of balancing against external perturbations. The framework embeds a kernel which is a model-based, full parametric closed-loop and analytical controller as the gait pattern generator. On top of that, a neural network with symmetric partial data augmentation learns to automatically adjust the parameters for the gait kernel, and also generate compensatory actions for all joints, thus significantly augmenting the stability under unexpected perturbations. Seven Neural Network policies with different configurations were optimized to validate the effectiveness and the combined use of the modulation of the kernel parameters and the compensation for the arms and legs using residual actions. The results validated that modulating kernel parameters alongside the residual actions have improved the stability significantly. Furthermore, The performance of the proposed framework was evaluated across a set of challenging simulated scenarios, and demonstrated considerable improvements compared to the baseline in recovering from large external forces (up to 118%). Besides, regarding measurement noise and model inaccuracies, the robustness of the proposed framework has been assessed through simulations, which demonstrated the robustness in the presence of these uncertainties. Furthermore, the trained policies were validated across a set of unseen scenarios and showed the generalization to dynamic walking

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalabilityComment: Code is available at https://github.com/xinge008/SS

Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey

Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research

Saw tooth cardiomyopathy: a case report

Saw tooth cardiomyopathy is an unusual and rare type of left ventricular dysplasia that is characterized by multiple projections of compacted myocardium that makes the appearance of �saw tooth� in noninvasive imaging. We present a young man with signs and symptoms of heart failure and reduced left ventricular function in echocardiography who showed distinctive left ventricle features of saw tooth cardiomyopathy (saw tooth appearance of myocardium in basal inferolateral and basal to mid lateral segments) in cardiac magnetic resonance imaging. © 2020 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology

Grounding language in perception for scene conceptualization in autonomous robots

In order to behave autonomously, it is desirable for robots to have the ability to use human supervision and learn from different input sources (perception, gestures, verbal and textual descriptions etc). In many machine learning tasks, the supervision is directed specifically towards machines and hence is straight forward clearly annotated examples. But this is not always very practical and recently it was found that the most preferred interface to robots is natural language. Also the supervision might only be available in a rather indirect form, which may be vague and incomplete. This is frequently the case when humans teach other humans since they may assume a particular context and existing world knowledge. We explore this idea here in the setting of conceptualizing objects and scene layouts. Initially the robot undergoes training from a human in recognizing some objects in the world and armed with this acquired knowledge it sets out in the world to explore and learn more higher level concepts like static scene layouts and environment activities. Here it has to exploit its learned knowledge and ground language into perception to use inputs from different sources that might have overlapping as well as novel information. When exploring, we assume that the robot is given visual input, without explicit type labels for objects, and also that it has access to more or less generic linguistic descriptions of scene layout. Thus our task here is to learn the spatial structure of a scene layout and simultaneously visual object models it was not trained on. In this paper, we present a cognitive architecture and learning framework for robot learning through natural human supervision and using multiple input sources by grounding language in perception

The RACE Project: Robustness by Autonomous Competence Enhancement

This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system

Pixel-Based Skin Detection for Pornography Filtering

A robust skin detector is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography filtering. Less than 10 years ago, the first paper on automatic pornography filtering was published. Since then, different researchers claim different color spaces to be the best choice for skin detection in pornography filtering. Unfortunately, no comprehensive work is performed on evaluating different color spaces and their performance for detecting naked persons. As such, researchers usualy refer to the results of skin detection based on the work doen for face detection, which underlies different imaging conditions. In this paper, we examine 21 color spaces in all their possible representations for pixel-based skin detection in pornographic images. Consequently, this paper holds a large investigation in the field of skin detection, and a specific run on the pornographic images

Modeling and Implementation of Omnidirectional Soccer Robot with Wide Vision Scope Applied in Robocup-MSL

The purpose of this paper is to design and implement a middle size soccer robot to conform RoboCup MSL league. First, according to the rules of RoboCup, we design the middle size soccer robot, The proposed autonomous soccer robot consists of the mechanical platform, motion control module, omni-directional vision module, front vision module, image processing and recognition module, investigated target object positioning and real coordinate reconstruction, robot path planning, competition strategies, and obstacle avoidance. And this soccer robot equips the laptop computer system and interface circuits to make decisions. In fact, the omnidirectional vision sensor of the vision system deals with the image processing and positioning for obstacle avoidance and<br />target tracking. The boundary-following algorithm (BFA) is applied to find the important features of the field. We utilize the sensor data fusion method in the control system parameters, self localization and world modeling. A vision-based self-localization and the conventional odometry<br />systems are fused for robust selflocalization. The localization algorithm includes filtering, sharing and integration of the data for different types of objects recognized in the environment. In the control strategies, we present three state modes, which include the Attack Strategy, Defense Strategy and Intercept Strategy. The methods have been tested in the many Robocup competition field middle size robots

Design and Implementation an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

Research on humanoid robotics in Mechatronics and Automation Laboratory, Electrical and Computer Engineering, Islamic Azad University Khorasgan branch (Isfahan) of Iran was started at<br />the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. This paper describes the hardware and software design of the kid size humanoid robot systems of the PERSIA Team in 2009. The robot has 20 actuated degrees of freedom based on Hitec HSR898. In this paper we have tried to focus on areas such as mechanical structure, Image processing unit, robot controller, Robot AI and behavior<br />learning. In 2009, our developments for the Kid size humanoid robot include: (1) the design and construction of our new humanoid robots (2) the design and construction of a new hardware and software controller to be used in our robots. The project is described in two main parts: Hardware and Software. The software is developed a robot application which consists walking controller, autonomous motion robot, self localization base on vision and Particle Filter, local AI, Trajectory Planning, Motion Controller and Network. The hardware consists of the mechanical structure and the driver circuit board. Each robot is able to walk, fast walk, pass, kick and dribble when it catches<br />the ball. These humanoids have been successfully participating in various robotic soccer competitions. This project is still in progress and some new interesting methods are described in the current report