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24 results for “Humanoid Robot”
IMU and marker-based optical motion capture from a humanoid robot
<p>The motion capture contains walking trials from the lower body of the humanoid robot Reem-C from Pal Robotics (Barcelona, Spain). Seven IMUs were attached on the foot, lower leg, upper leg and pelvis segments. IMU data was collected at 100 Hz. Moreover, the robot motion was captured with a marker-based optical system (Qualisys AB, Göteborg, Sweden) at 150 Hz. The focus of the dataset was mainly walking. There are three trials, each with a length of about 6.5 minutes.<br>The dataset contains the definition of the skeleton (segment lengths and coordinate locations), the actual IMU readings and the pose or kinematics from the optical system.</p>
Figure 5. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The behavior module determines the target position and orientation according to the results<br> of localization and the sensor measurements, and then constructs an action series which consists of<br> the elementary gaits to realize omni directional walking. The implementation of forward walking is<br> applying Virtual Slope Walking in the sagittal plane with the Lateral Swing Movement for lateral<br> stability. The sideward walking and turning is realized by carefully designing the key frames. All of<br> above gait is generated by connecting the key frames with smooth sinusoids. The forward walking<br> speed of PERSIA Humanoid Robot is 25cm/s. The image sequences of forward walking are shown<br> in Figure 5.</p>
Figure 2. Mechanical construction of the PERSIA humanoid robots-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 2 shows one of the constructions used for our robots. Knee joints are considered to<br> bend in both directions which help faster response of the robot in backward walking. Efforts have<br> been made to hold the proportions as much as possible human like. The PERSIA robot is 38cm tall<br> and weighs about 1.6 kg. It has 18 degrees of freedom: 5 in each leg, 3 in each hand and 2 in head.<br> To facilitate exchange of the players, all robots use mechanically the same structure.</p>
Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>Figure 4 shows the block diagram of the software which runs in the robot’s main processor.<br> The program consists of 4 main blocks:<br> • Hardware Interface: Contains all low level routines to access hardware of the robot including<br> sensors and actuators.<br> • Vision: Contains image processing algorithms such as recognition of landmarks and other<br> object. Self localization is done using particle filtering. Particles are scored by comparing a<br> simulated image from each particle with the current frame captured by camera. Using<br> “Sampling-Importance Resampling” method, a new distribution of the particles is created after<br> each step.<br> Particles are also updated using a motion model. Final distribution of the particles converges to<br> the real pose of the robot.<br> • Planning: Planning system of the robot is based on a multi layer, and multi thread structure.<br> The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario<br> which runs in parallel with the scenarios in the other layers. A scenario in a higher level can<br> terminate and change the scenario running in the lower level; however it is usually done in<br> synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as<br> stopping the walking motion while one of the feet is still in the air).<br> • Network: Mainly responsible for the wireless communication of the robot with the other robots<br> or the referee box. This is done via WLAN.<br> • Motion Control: manages all the actuators of the robot, and controls locomotion or any other<br> action of the robot according to the requests from Cognition.<br> • Sensor Control: manages other sensors, and interacts with the Sub-Controller.</p>
Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>This module receives information from Artificial Intelligent unit. Total functions about<br> Robot Behavior such as stability motors actions, robot path planning, turn camera, walking,<br> shooting, dribbling; motion and etc are controlled in this section.</p>
Figure 1. PERSIA Humanoid Robot in Robocup IranOpen2010 Competition-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>In this paper, we will at first describe the general hardware design of the PERSIA Humanoid<br> Robocup Team, (section 2) and after that focus on our scientific approaches in sensor fusion and<br> learning (section 3). Finally, section 4 concludes this paper. This document describes the current<br> state of the project as well as the intended development for the RoboCup 2010 competition.</p>
Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The PERSIA Humanoid robot designed for has multipurpose capability. This robot<br> equipped with main board for motion control, vision sensor, other balancing sensors, servo motors<br> and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control<br> system.</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 4. Intelligent robots (accessed 01.11.2017). 4.1. Erica, a humanoid robot (https://www.tech-review.com/erica-is-the-latest-japanese-robot-with-human-appearance.html). 4.2. Atlas, a bipedal humanoid robot developed by Boston Dynamics (https://en.wikipedia.org/wiki/Atlas_(robot))
<p>One of the most highly quoted and interesting definitions of machine intelligence was presented by Alan Turing (1950). Turing considered a computing system intelligent if a human assessor could not decide the nature of the system (being human or artificial) based on questions asked from a room hidden from a human assessor. Until recently there were performed different discussions and comments on the Turing test. Hernández-Orallo (2000) presents an interesting study related to the Turing Test. Dowe and Hajek, (1998) propose a computational extension of the Turing Test. The design and development of intelligent systems are historically very recent. But, even if the advance of hardware and software is very fast, it will take a longer time until the artificial computing systems will attain a similar intelligence with the humans. Based on this fact, we consider that is not appropriate to formulate the problem of the direct comparison at a general level of human intelligence with the machine intelligence. Different definitions were proposed for the intelligence of the agents (Russell, & Norvig, 2003; Iantovics, & Zamfirescu, 2013). Many authors (Russell, & Norvig, 2003; Iantovics, 2005) argue that the intelligence of the agents cannot be defined universally. The impossibility to give a universal definition to the human intelligence is based mostly on the enormous complexity of the human brain and complexity of the human thinking and decision making. Similarly, we may consider the impossibility of universal definition of intelligence of the agents based on the very large variety (by type and complexity) of intelligent agents. The machine intelligence frequently is defined based on different abilities such as (Iantovics, 2005; Sharkey, 2006): autonomous learning, self-adaptation, and evolution. These principles of considering the intelligence are inspired by biological life forms able to learn autonomously during their life cycle, to adapt to the environment and to evolve during more generations. We would like to outline that not all the designed agents are intelligent. There is not a required property of an agent to be intelligent.</p>
Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The image sequences of sideward walking and turning are shown in Figure 6 respectively.</p>
Event-Driven Figure-Ground organisation model for the humanoid robot iCub
<p>Figure-ground organisation is a perceptual grouping mechanism for detecting objects and boundaries, essential for an agent interacting with the environment. Current figure-ground segmentation methods rely on classical computer vision or deep learning, requiring extensive computational resources, especially during training. Inspired by the primate visual system, we developed a bio-inspired perception system for the neuromorphic robot iCub. The model uses a hierarchical, biologically plausible architecture and event-driven vision to distinguish foreground objects from the background. Unlike classical approaches, event-driven cameras reduce data redundancy and computation. The system has been qualitatively and quantitatively assessed in simulations and with event-driven cameras on iCub in various scenarios. It successfully segments items in diverse real-world settings, showing comparable results to its frame-based version on simple stimuli and the Berkeley Segmentation dataset. This model enhances hybrid systems, complementing conventional deep learning models by processing only relevant data in Regions of Interest (ROI), enabling low-latency autonomous robotic applications.</p>
Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)
<pre><code class="language-markdown">This dataset contains data accompanying the work: @ARTICLE{9676410, author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele}, journal={IEEE Robotics and Automation Letters}, title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots}, year={2022}, volume={7}, number={2}, pages={2779-2886}, doi={10.1109/LRA.2022.3141658}} The dataset is organized in folders, whose content can be summarized as follows: - mocap: motion capture data collected from human motion - retargeted_mocap: motion capture data retargeted on the robot - IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator - training_D2_D3_subsampled_mirrored_4ew_98%: training data - inference: data collected while generating trajectories - trajectory_control_simulation: data collected while controlling trajectories in simulation - trajectory_control_real_robot: data collected while controlling trajectories on the real robot - additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.</code></pre>
Reconstruction of car–running pedestrian accident based on humanoid robot method
<p>This is the appendix of the manuscript titled "Reconstruction of car–running pedestrian accident based on humanoid robot method", which will help the reviewers to better understand the manuscript. It is also the supporting part of this manuscript.</p>
Effect of a Humanoid Robot With Virtual Reality Games to Train Arm Function in Children With Cerebral Palsy - THRIVE Trial
ClinicalTrials.gov study NCT06881407. IPD Sharing: YES. Countries: 1. Publications: 3.
The Presence of Humanoid Robot With Older Adults at Homes
ClinicalTrials.gov study NCT06487611. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessment of the Acceptability of a Humanoid Robot at Home for Children With Cochlear Implants - H2R2
ClinicalTrials.gov study NCT04832373. IPD Sharing: NO. Countries: 1. Publications: 2.
Using a Humanoid Robot to Distract Children With Chronic Conditions Undergoing Painful Procedures
ClinicalTrials.gov study NCT04003701. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Collaborative sorting task between the humanoid robot Neachy and a human partner
<p>Picture taken by © Pierre DE PARSCAU / ETIS / CNRS Images.</p>
Humanoid Robot vs Treatment as Usual for Loneliness
ClinicalTrials.gov study NCT05423899. IPD Sharing: NO. Countries: 0. Publications: 1.
The Role of Humanoid Robot in Neuropsychological Assessment and Cognitive Training.
ClinicalTrials.gov study NCT05788133. IPD Sharing: NO. Countries: 1. Publications: 0.
Deployment and Evaluation of Humanoid Robotics in Coalition-Aligned Clinical Care Settings
ClinicalTrials.gov study NCT07214896. IPD Sharing: YES. Countries: 3. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.