Understanding the Importance and Challenges of Animatronic Humanoid Prototypes Production in the Robotic Field in the United of States of America: Policy Implications

This study analyzes the importance and challenges of animatronic humanoid prototypes production within the robotic field in the United States of America to influence Artificial Intelligence (AI) policy. In fact, animatronic humanoid prototype has greatly inspired more designers and developers’ interest in the study of human and robot interaction in both scientists and enthusiast alike to aid in robotic production. The study adopted a narrative literature review and Boolean search technique to identify 22 researches and review articles that are related to applications, challenges and importance of animatronic humanoid robotics production and applications. As part of the findings for the study utilized for the article, many scholars made specific inferences to the robotic applications in firms, businesses, and nations. Out of the twenty-two articles, five of the researchers, thus 22%, underscored and also perceived that robots and machines with biped locomotion is one of the achievements of humanoid robotic production. Researchers of 4 articles—thus 18% -- explicitly stated in their research that one of the achievements of humanoid robotic production is “learning capabilities in robots”. The study further revealed that some of the evidenced-based research applications for humanoid robotic products include the following: Mitsuo Kawato of ATR Japan proposed using humanoid robots to study human behavior; In Europe- EU-funded projects, which include the large-scale NEUROBOTICS project; RobotCub project; Human Brain Project; Atlas humanoid robot by Boston Dynamics; and Wisdom of the robot Sophia in engaging in conversations, etc. Above all, this study revealed that complex environment, perception, human robot interaction, and collaboration in real life are some of the challenges identified in the literature. Therefore, in order to overcome such challenges—for the body of a robot, designers need to rethink the materials that robots are made of and leverage morphological computation to intrinsically balance and compensate for motion and dynamic behavior. Also, investors, policymakers, and public officials should invest more in innovative robotic production in order to promote businesses, bring about efficiency in operations, and to increase productivity. Keywords:Humanoid, Locomotion, Robots, Prototypes, Production, Animatronic, Boolean-Search, Jobs, Artificial Intelligence DOI: 10.7176/ISDE/13-1-02 Publication date:March 31st 202

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.Comment: 8 pages, 7 figures, double col, ieee conference forma