scispace - formally typeset
Search or ask a question
Topic

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: 🤖.


Papers
More filters
Proceedings ArticleDOI
23 Jun 2013
TL;DR: The proposed model is implemented into a humanoid robot and is confirmed as effective in a field experimen and established a model for a robot to perform natural handing.
Abstract: This paper reports our research on developing a model for a robot distributing flyers to pedestrians. The difficulty is that potential receivers are pedestrians who are not necessarily cooperative; thus, the robot needs to appropriately plan its motion, making it is easy and non-obstructive for potential receivers to receive the flyers. In order to establish the model, we observed human interactions on distributional handing in the real world. We analyzed and evaluated different handing methods that people perform, and established a model for a robot to perform natural handing. The proposed model is implemented into a humanoid robot and is confirmed as effective in a field experimen

60 citations

Proceedings ArticleDOI
12 Dec 2011
TL;DR: This paper explores the application of hierarchical BCIs to the task of controlling a PR2 humanoid robot and teaching it new skills, and presents the first demonstration of training a hierarchical BCI for a non-navigational task.
Abstract: Recent advances in neuroscience and humanoid robotics have allowed initial demonstrations of brain-computer interfaces (BCIs) for controlling humanoid robots. However, previous BCIs have relied on higher-level control based on fixed pre-wired behaviors. On the other hand, low-level control can be tedious, imposing a high cognitive load on the BCI user. To address these problems, we previously proposed an adaptive hierarchical approach to brain-computer interfacing: users teach the BCI system new skills on-the-fly; these skills can later be invoked directly as high-level commands, relieving the user of tedious control. In this paper, we explore the application of hierarchical BCIs to the task of controlling a PR2 humanoid robot and teaching it new skills. We further explore the use of explicitly-defined sequences of commands as a way for the user to define a more complex task involving multiple state spaces. We report results from three subjects who used a hierarchical electroencephalogram (EEG)-based BCI to successfully train and control the PR2 humanoid robot in a simulated household task maneuvering the robot's arm to pour milk over a bowl of cereal. We present the first demonstration of training a hierarchical BCI for a non-navigational task. This is also the first demonstration of using one to train a more complex task involving multiple state spaces.

60 citations

Proceedings ArticleDOI
08 Jun 2005
TL;DR: In this paper, a provably asymptotically stabilizing controller that integrates the fully-actuated and underactuated phases of walking is proposed, which is based on the hybrid zero dynamics of Westervelt et al.
Abstract: This paper addresses the key problem of walking with both fully-actuated and underactuated phases. The studied robot is planar, bipedal, and fully actuated in the sense that it has feet with revolute, actuated ankles. The desired walking motion is assumed to consist of three successive phases: a fully-actuated phase where the stance foot is flat on the ground, an underactuated phase where the stance heel lifts from the ground and the stance foot rotates about the toe, and an instantaneous double support phase where leg exchange takes place. The main contribution of the paper is to provide a provably asymptotically stabilizing controller that integrates the fully-actuated and underactuated phases of walking. By comparison, existing humanoid robots, such as ASIMO and Qrio, use only the fully-actuated phase (i.e., they only execute flat-footed walking), or RABBIT, which uses only the underactuated phase (i.e., it has no feet, and hence walks as if on stilts). The controller proposed here is organized around the hybrid zero dynamics of Westervelt et al. (2003) in order that the stability analysis of the closed-loop system may be reduced to a one-dimensional Poincare map that can be computed in closed form.

60 citations

Journal ArticleDOI
TL;DR: The proposed unsupervised automatic facial point detection integrated with regression-based intensity estimation for facial action units (AUs) and emotion clustering to deal with such challenges outperforms other supervised models.

60 citations

Proceedings ArticleDOI
13 Oct 1998
TL;DR: A model of human visual attention called FeatureGate is described, which is a locally connected, pyramidal, artificial neural network that operates on 2D feature maps of the environment that finds the location whose features most closely match those of the target.
Abstract: For a humanoid robot to interact easily with a person, the robot should have human-like sensory capabilities and attentional mechanisms. Particularly useful is an active vision head controlled by a visual attention system that selects viewpoints in the environment as a function of the robot's task. This paper describes a model of human visual attention called FeatureGate, which is a locally connected, pyramidal, artificial neural network that operates on 2D feature maps of the environment. Given a set of feature maps, and the description of a specific target, FeatureGate finds the location whose features most closely match those of the target. The paper describes the network, its implementation, a series of tests that characterize its performance with respect to a person's performance on a similar task, and its use in the control of an active vision system.

60 citations


Network Information
Related Topics (5)
Mobile robot
66.7K papers, 1.1M citations
96% related
Robot
103.8K papers, 1.3M citations
95% related
Adaptive control
60.1K papers, 1.2M citations
84% related
Control theory
299.6K papers, 3.1M citations
83% related
Object detection
46.1K papers, 1.3M citations
81% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023253
2022759
2021573
2020647
2019801
2018921