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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: A novel robotic interaction system capable of administering and adjusting joint attention prompts to a small group of children with ASD, highlighting both potential benefits of robotic systems for directed intervention approaches as well as potent limitations of existing humanoid robotic platforms.
Abstract: Although it has often been argued that clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorder (ASD), relatively few investigations have indexed the impact of intervention and feedback approaches. This pilot study investigated the application of a novel robotic interaction system capable of administering and adjusting joint attention prompts to a small group (n = 6) of children with ASD. Across a series of four sessions, children improved in their ability to orient to prompts administered by the robotic system and continued to display strong attention toward the humanoid robot over time. The results highlight both potential benefits of robotic systems for directed intervention approaches as well as potent limitations of existing humanoid robotic platforms.

138 citations

Proceedings ArticleDOI
10 Dec 2007
TL;DR: A robust model-based three-dimensional tracking system by programmable graphics hardware to operate online at frame-rate during locomotion of a humanoid robot and recovers the full 6 degree-of- freedom pose of viewable objects relative to the robot.
Abstract: For humanoid robots to fully realize their biped potential in a three-dimensional world and step over, around or onto obstacles such as stairs, appropriate and efficient approaches to execution, planning and perception are required. To this end, we have accelerated a robust model-based three-dimensional tracking system by programmable graphics hardware to operate online at frame-rate during locomotion of a humanoid robot. The tracker recovers the full 6 degree-of- freedom pose of viewable objects relative to the robot. Leveraging the computational resources of the GPU for perception has enabled us to increase our tracker's robustness to the significant camera displacement and camera shake typically encountered during humanoid navigation. We have combined our approach with a footstep planner and a controller capable of adaptively adjusting the height of swing leg trajectories. The resulting integrated perception-planning-action system has allowed an HRP-2 humanoid robot to successfully and rapidly localize, approach and climb stairs, as well as to avoid obstacles during walking.

138 citations

Proceedings ArticleDOI
10 Dec 2002
TL;DR: This paper presents a method for importing human dance motion into humanoid robots through visual observation and tries to make a humanoid dance these original or generated motions using inverse-kinematics and dynamic balancing techniques.
Abstract: This paper presents a method for importing human dance motion into humanoid robots through visual observation. The human motion data is acquired from a motion capture system consisting of 8 cameras and 8 PC clusters. Then the whole motion sequence is divided into motion elements and clustered into groups according to the correlation of end-effector trajectories. We call these segments 'motion primitives'. New dance motions are generated by concatenating these motion primitives. We are also trying to make a humanoid dance these original or generated motions using inverse-kinematics and dynamic balancing techniques.

137 citations

Proceedings ArticleDOI
21 May 2001
TL;DR: An approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals from a geometric model of the environment and a statically-stable desired posture is presented.
Abstract: We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using the humanoid robot "H6".

137 citations

Proceedings ArticleDOI
20 Sep 2004
TL;DR: In this paper, the authors examined children's perceptions of robots in terms of physical attributes, personality and emotion traits, and found that children clearly distinguish between emotions and behaviour when judging robots.
Abstract: Our study considers children's perceptions of robots in terms of physical attributes, personality and emotion traits. To examine children's attitudes towards robots, a questionnaire approach was taken with a large sample of children, followed by a detailed statistical framework to analyse the data. Results show that children clearly distinguish between emotions and behaviour when judging robots. The distinguishing robotic physical characteristics for positive and negative emotions and behaviour are highlighted. Children judge human-like robots as aggressive, but human-machine robots as friendly providing support for the uncanny valley. The paper concludes with discussing the results in light of design implications for children's robots.

137 citations


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