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Humanoid robot

About: Humanoid robot is a research topic. Over the lifetime, 14387 publications have been published within this topic receiving 243674 citations. The topic is also known as: 🤖.


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Journal Article•DOI•
25 Oct 2004
TL;DR: A humanoid robot, HERMES, is developed to study several key technologies that are important for personal robots, such as robot design, sensors and perception, locomotion, localization and navigation, manipulation, human-robot communication and interaction, adaptability and learning, system architecture and integration, and dependability.
Abstract: We have developed a humanoid robot, HERMES, to study several key technologies that are important for personal robots, such as robot design, sensors and perception, locomotion, localization and navigation, manipulation, human-robot communication and interaction, adaptability and learning, system architecture and integration, and dependability. The robot's skill-based system architecture was derived from a qualitative model of human information processing and insights gained from psychological literature dealing with skill acquisition, human performance and motor learning. HERMES' system architecture, several of its skills and the design principles are introduced, and some experiments carried out with the real robot are presented, including a long-term test where HERMES served in a museum, far away from its home laboratory, for more than six months up to 12 hours per day. During this period the robot and its skills were regularly demonstrated to the public by nonexpert presenters. Also, HERMES interacted with the visitors, chatted with them in English, French and German, answered questions and performed services as requested by them.

76 citations

Journal Article•DOI•
TL;DR: Three state of art techniques are used as artificial neural network, extreme learning machine and deep neural network learning based CNN mode for the classification purpose and the model classification accuracy is obtained as 87.4%, 88% and 92%, respectively.
Abstract: A bipedal walking robot is a kind of humanoid robot. It is suppose to mimics human behavior and designed to perform human specific tasks. Currently, humanoid robots are not capable to walk like human being. To perform the walking task, in the current work, human gait data of six different walking styles named brisk walk, normal walk, very slow walk, medium walk, jogging and fast walk is collected through our configured IMU sensor and mobile-based accelerometers device. To capture the pattern for six different walking styles, data is extracted for hip, knee, ankle, shank, thigh and foot. A total six classes of walking activities are explored for clinical examination. The accelerometer is placed at center of the human body of 15 male and 10 female subjects. In the experimental setup, we have done exploratory analysis over the different gait capturing techniques, different gait features and different gait classification techniques. For the classification purpose, three state of art techniques are used as artificial neural network, extreme learning machine and deep neural network learning based CNN mode. The model classification accuracy is obtained as 87.4%, 88% and 92%, respectively. Here, WISDM activity data set is also used for verification purpose.

76 citations

Proceedings Article•
01 Jan 2000
TL;DR: This paper addresses the mechanism design methodologies, specification, and control strategies of a mobile manipulation system for the humanoid robot ARMAR, that has to work autonomously or interactively in cooperation with humans in dynamic unstructured environments such as workshops or homes.
Abstract: This paper addresses the mechanism design methodologies, specification, and control strategies of a mobile manipulation system for the humanoid robot ARMAR, that has to work autonomously or interactively in cooperation with humans in dynamic unstructured environments such as workshops or homes.

76 citations

Proceedings Article•DOI•
01 Jan 2010

76 citations

Journal Article•DOI•
TL;DR: A large-scale database of whole-body human motion with methods and tools which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments is presented.
Abstract: Large-scale human motion databases are key for research questions ranging from human motion analysis and synthesis, biomechanics of human motion, data-driven learning of motion primitives, and rehabilitation robotics to the design of humanoid robots and wearable robots such as exoskeletons. In this paper we present a large-scale database of whole-body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments. To this end, captured subject-specific motion is normalized regarding the subject's height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM). In contrast with previous approaches and human motion databases, the motion data in our database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, we present procedures and techniques for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject–object relations. To allow efficient search for certain motion types in the database, motion recordings are manually annotated with motion description tags organized in a tree structure. We demonstrate the transfer of human motion to humanoid robots and provide several examples of motion analysis using the database.

76 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023253
2022759
2021573
2020647
2019801
2018921