Intelligent Humanoids Research Articles

Tactile and Vision Perception for Intelligent Humanoids

Intelligent Humanoids In article number 2100074, Shuo Gao, Yanning Dai, and Arokia Nathan present a review on the working mechanisms of tactile and visual sensing, introduce the application of intelligent humanoids in different scenarios, and discuss the current challenges and future trends. The cover image depicts a humanoid working on the surface of the Moon, while the background is the Earth in the universe. Besides, the humanoid is part of a spacecraft and some scientific instrumentation. The image emphasizes that humanoid can conduct long-term and dangerous tasks with the perceptual functions of visual and tactile.

Tactile and Vision Perception for Intelligent Humanoids

Touch and vision perception are two important functions humans use to interact with the real world. To mimic human‐like abilities, tactile‐ and visual‐sensing‐based intelligent humanoids have emerged and are going through a fast phase of development. Studies have demonstrated that the combination of tactile and visual information not only enables humanoids to better learn the environment, but also allows them to have pseudocognitive ability. Being a new and rapidly developing field of research, a significant growth in articles reporting different aspects of sensing and related machine learning is being witnessed. To help readers comprehensively understand the fundamentals and insights, and the current state of the art, this review is compiled to explain the working mechanisms of tactile and visual sensing, introduce the application of intelligent humanoids in diverse scenarios, discuss current challenges, and predict future trends.

Frontier concept of rabi chip for intelligent humanoid

The sequence of the human brain can be configured by the originated strongly coupling fields to a pair of the ionic substances(bio-cells) within the microtubules. From which the dipole oscillation begins and transports by the strong trapped force, which is known as a tweezer. The tweezers are the trapped polaritons, which are the electrical charges with information. They will be collected on the brain surface and transport via the liquid core guide wave, which is the mixture of blood content and water. The oscillation frequency is called the Rabi frequency, is formed by the two-level atom system. Our aim will manipulate the Rabi oscillation by an on-chip device, where the quantum outputs may help to form the realistic human brain function for humanoid robotic applications.

Recognition and prediction of manipulation actions using Enriched Semantic Event Chains

Human activity understanding has attracted much attention in recent years, because it plays a key role in a wide range of applications such as human–computer interfaces, visual surveillance, video indexing, intelligent humanoids robots, ambient intelligence and more. Activity understanding strongly benefits from fast, predictive action recognition. Here we present a new prediction algorithm for manipulation action classes in natural scenes. Manipulations are first represented by their temporal sequence of changing static and dynamic spatial relations between the objects that take part in the manipulation. This creates a transition matrix, called “Enriched Semantic Event Chain (ESEC)”. We use these ESECs to classify and predict a large set of manipulations. We find that manipulations can be correctly predicted after only (on average) 45% of their total execution time and that we are almost twice as fast as a standard HMM-based method used for comparison.

Sound and Visual Tracking for Humanoid Robot

Mobile robots capable of auditory perception usually adopt the “stop-perceive-act” principle to avoid sounds made during moving due to motor noise. Although this principle reduces the complexity of the problems involved in auditory processing for mobile robots, it restricts their capabilities of auditory processing. In this paper, sound and visual tracking are investigated to compensate each other's drawbacks in tracking objects and to attain robust object tracking. Visual tracking may be difficult in case of occlusion, while sound tracking may be ambiguous in localization due to the nature of auditory processing. For this purpose, we present an active audition system for humanoid robot. The audition system of the highly intelligent humanoid requires localization of sound sources and identification of meanings of the sound in the auditory scene. The active audition reported in this paper focuses on improved sound source tracking by integrating audition, vision, and motor control. Given the multiple sound sources in the auditory scene, SIG the humanoid actively moves its head to improve localization by aligning microphones orthogonal to the sound source and by capturing the possible sound sources by vision. The system adaptively cancels motor noises using motor control signals. The experimental result demonstrates the effectiveness of sound and visual tracking.

Issues in Humanoid Audition and Sound Source Localization by Active Audition.

In this paper, we present an active audition system which is implemented on the humanoid robot the humanoid. The audition system for highly intelligent humanoids localizes sound sources and recognizes auditory events in the auditory scene. Active audition reported in this paper enables SIG to track sources by integrating audition, vision, and motor movements. Given the multiple sound sources in the auditory scene, SIG actively moves its head to improve localization by aligning microphones orthogonal to the sound source and by capturing the possible sound sources by vision. However, such an active head movement inevitably creates motor noises.The system adaptively cancels motor noises using motor control signals and the cover acoustics. The experimental result demonstrates that active audition by integration of audition, vision, and motor control attains sound source tracking in variety of conditions.onditions.