BALANCE MOTION PLANNING FOR HUMANOID ROBOT BASED ON 3D INVERTED PENDULUM MODEL

Researches on humanoid robots are alway attractive to many researchers in robotics field. One of considerable challenges of humanoid robots is to keep balance and stability of their movement. Because a humanoid robot moves by two legs, most of time of the step period of the humanoid robot is be in one leg touching on the floor and the other leg swinging forward. This posture is similar to a three dimension (3D) inverted pendulum model. This papers presents the dynamic model of a 3D inverted pendulum model and applies to balanced motion planning for a humanoid robot. The obtained results show that the robot is able to keep balance during its movements

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots’ joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots.

Identification of COM Controller of a Human in Stance Based on Motion Measurement and Phase-Space Analysis

This article proposes a process to identify the standing stabilizer, namely, the controller in humans to keep upright posture stable against perturbations. We model the controller as a piecewise-linear feedback system, where the state of the center of mass (COM) is regulated by coordinating the whole body so as to locate the zero-moment point (ZMP) at the desired position. This was developed for humanoid robots and is possibly able to elaborate the fundamental control scheme used by humans to stabilize themselves. Difficulties lie on how to collect motion trajectories in a wide area of the state space for reliable identification and how to identify the piecewise-affine dynamical system. For the former problem, a motion measurement protocol is devised based on the theoretical phase portrait of the system. Regarding the latter problem, some clustering techniques including K-means method and EM (Expectation-and-Maximization) algorithm were examined. We found that a modified K-means method produced the most accurate result in this study. The method was applied to the identification of a lateral standing controller of a human subject. The result of the identification quantitatively supported a hypothesis that the COM-ZMP regulator reasonably models the human’s controller when deviations of the angular momentum about the COM are limited.

Stabilized Walking of Humanoid NAO using Enhanced Spring-Loaded Inverted Pendulum Model on Uneven Terrain

In the coming decades, humanoid robots will play a rising role in society. The present article discusses their walking control and obstacle avoidance on uneven terrain using enhanced spring-loaded inverted pendulum model (ESLIP). The SLIP model is enhanced by tuning it with an adaptive particle swarm optimization (APSO) approach. It helps the humanoid robot to reach closer to the obstacles in order to optimize the turning angle to optimize the path length. The desired trajectory, along with the sensory data, is provided to the SLIP model, which creates compatible COM (center of mass) dynamics for stable walking. This output is fed to APSO as input, which adjusts the placement of the foot during interaction with uneven surfaces and obstacles. It provides an optimum turning angle for shunning the obstacles and ensures the shortest path length. Simulation has been carried out in a 3D simulator based on the proposed controller and SLIP controller in uneven terrain.

Existing Robotics Technologies for Implementation of Special Education

Collaborative robots (Cobots) are described from the point of view of the cognitive processes underlying the perception and emotional expression of learners based on individual human interacting with non-humanoid robots. The chapter describes a project that is aimed at the development and prototyping of mobile cognitive robotic system designed for service and assistance to people with disabilities. In creating this robot called “AnRI” (anthropomorphic robot intelligent) the experience from building the previous one was used, and it was used in the project Conduct Research into the Adoption of Robotic Technologies in Special Education by Children, Young People, and Pedagogical Specialists. It is described as a device of the robot and realization of cognitive processes to integrate knowledge-related information from sensors, actuators, and multiple sources of information vital to the process of serving people with disabilities.

Xenobots – A remarkable combination of Artificial Intelligence based biological living robot

Technology is improving day by day and every new face of it is engrossing, making applied science astonishment. Robotics and Artificial Intelligence have taken the world beyond automation. Automation was once considered as a challenge, but now the same technology has stunned the whole world, with the transformation of vision to the reality of live cell robots. In this modern era, evolutionary algorithms with Artificial Intelligence have made an impact on the automation and the creation of rare live-cell species by integrating biological aspects of frog cells. It would be thus useful in various domains to build technologies using self-renewing, and biocompatible materials of which the ideal candidates are living themselves. Thus, this paper presents a live cell robot named Xenobots, its design method, formation, applications, and transformation of live cell robots to humanoid robots that mimic the human brain.

Populism in Robots

Populism has rarely been discussed in the context of robotics. This chapter will explore how intimate relationships, such as love and sex, between human and robots, will bring populism to the populist masses. Intimacy between humans and robots was first raised and discussed by David Levy in his book titled Love and Sex with Robotics published in 2007. As a result, the subject of human-robot romantic and intimate relationships rapidly developed into an academic research discipline in its own right. Since then, researchers have come up with many implementations of robot companions like sex robots, emotional robots, humanoid robots, and artificial intelligent systems that can simulate human emotions. This chapter presents a summary of significant activity in this field during the recent years, predicts how the field is likely to develop, and its ethical and legal background. They also discuss their research in physical devices for human-robot love and sex communication.