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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 ArticleDOI
TL;DR: It is shown that such invariants emerge from the underlying physics of the task, using simulation data, and are actually useful for generating robot motion, which has been successfully realized with an adult-size real humanoid robot.

56 citations

Journal ArticleDOI
TL;DR: In this paper, the bipedal walk is configured as the rocking block model, and the hip, knee, and ankle trajectories have been synthesized from the model and the stability margin has been defined analytically.
Abstract: Human walk is the combination of seven different discrete subphases. It is difficult to express the one gait cycle as a whole. To develop the human like bipedal robot, the walk cycle is divided into seven discrete subphases. Each subphases has its own continuous dynamics. To express this discrete behavior for the development of the more accurate bipedal robot, the hybrid automata are proposed. The bipedal walk is configured as the rocking block model. It is the first attempt to express the bipedal walk as a rocking block. During double support phases, it is configured as a vertical rectangular plane, and during the left and right leg swing, it is configured as the tilt of the rectangular rocking block in the left and right direction. In this paper, we have configured the bipedal robot as the rocking block before and after impact. The novelty of work is the configuration of bipedal walk as the rocking block and the development of hybrid automata. We configured the hybrid automata dynamic walk model for individual subjects. The trajectory generated by the model is compared with the two models of OpenSim bipedal Gait2354 and normal walk. This paper presents a new modeling technique of bipedal locomotion using hybrid automata. The hip, knee, and ankle trajectories have been synthesized from the model. The stability margin has been defined analytically. Similarly, these trajectories have been fed to a real humanoid robot HOAP2, which were able to perform the stable walking with these trajectories.

56 citations

Proceedings ArticleDOI
10 Dec 2007
TL;DR: A connectionist model that combines motions and language based on the behavioral experiences of a real robot is presented that demonstrated that the robot could generate a motion sequence corresponding to given linguistic sequence even if the motions or sequences were not included in the training data, and vice versa.
Abstract: We present a connectionist model that combines motions and language based on the behavioral experiences of a real robot. Two models of recurrent neural network with parametric bias (RNNPB) were trained using motion sequences and linguistic sequences. These sequences were combined using their respective parameters so that the robot could handle many-to-many relationships between motion sequences and linguistic sequences. Motion sequences were articulated into some primitives corresponding to given linguistic sequences using the prediction error of the RNNPB model. The experimental task in which a humanoid robot moved its arm on a table demonstrated that the robot could generate a motion sequence corresponding to given linguistic sequence even if the motions or sequences were not included in the training data, and vice versa.

56 citations

Proceedings ArticleDOI
01 Oct 2006
TL;DR: A very generic architecture of this motion planner, highly modular, as well as a first implementation of it are described, for a simple grasping task using the HRP-2 humanoid robot.
Abstract: This paper deals with the motion planning of a poly-articulated robotic system for which support contacts are allowed to occur between any part of the body and any part of the environment. Starting with a description of the environment and of a target, it computes a sequence of postures that allow our system to reach its target. We describe a very generic architecture of this planner, highly modular, as well as a first implementation of it. We then present our results, both simulations and real experiments, for a simple grasping task using the HRP-2 humanoid robot.

56 citations

Journal ArticleDOI
TL;DR: The present study demonstrates the last step of a general process for developing emotional postures for robots, which starts with qualitative descriptions of human postures, continues with encoding those descriptions in quantitative terms, and ends with adaptation of the quantitative values to a specific robot.
Abstract: This paper presents the development of emotional postures for the humanoid robot Nao. The approach is based on adaptation of the postures that are developed for a virtual human body model to the case of the physical robot Nao. In the paper the association between the joints of the human body model and the joints of the Nao robot are described and the transformation of postures is explained. The non-correspondence between the joints of the actual physical robot and the joints of the human body model was a major challenge in this work. Moreover, the implementation of the postures into the robot was constrained by the physical structure and the artificial mass distribution. Postures for the three emotions of anger, sadness, and happiness are studied. Thirty two postures are generated for each emotion. Among them the best five postures for each emotion are selected based on the votes of twenty five external observers. The distribution of the votes indicates that many of the implemented postures do not convey the intended emotions. The emotional content of the selected best five postures are tested by the votes of forty observers. The intended emotions received the highest recognition rate for each group of these selected postures. This study can be considered to be the last step of a general process for developing emotional postures for robots. This process starts with qualitative descriptions of human postures, continues with encoding those descriptions in quantitative terms, and ends with adaptation of the quantitative values to a specific robot. The present study demonstrates the last step of this process.

56 citations


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