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Simulation and experimental results on the humanoid robot ASIMO will underline the potential of the proposed approach.
Experiments performed with the humanoid robot ASIMO show that the proposed system is suitable for transferring information from a human demonstrator to the robot.
, – This shows that many AI concepts are being applied to humanoid, mobile and other classes of robots.
Open accessProceedings ArticleDOI
05 Aug 2007
13 Citations
The project shows how robots can interact with humans in subtle and sustainable ways for entertainment and enjoyment.
Open accessBook ChapterDOI
James Hereford, Michael A. Siebold 
01 Mar 2010
38 Citations
The upper limit on the number of robots will be set by the communication links among the robots; there needs to be a way to share information among the robots without requiring lots of communication traffic.
Proceedings ArticleDOI
Avinash Gautam, Sudeept Mohan 
17 Sep 2012
86 Citations
In these kinds of systems robots are far less capable as an entity, but the real power lies in cooperation of multiple robots.
We also show that these models are able to discover many camouflaged and previously unidentified robots.
Open accessJournal ArticleDOI
Tariq Iqbal, Laurel D. Riek 
23 Feb 2017
31 Citations
However, how robots engage in this process can influence the dynamics of the team, particularly in multihuman, multirobot situations.
Open accessProceedings ArticleDOI
Judith Müller, Udo Frese, Thomas Röfer 
01 Nov 2012
27 Citations
Compared to skilled robots such as Justin [2] or ASIMO [3] online grasping with the Nao constitute as particular problem due to the limited processing power and the hand design.
from state, national and international competitions suggest that many of the children who participate in the activities supported by ITEE are subsequently able to purpose- build robots to effectively compete in RoboCupJunior competitions.

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Why Sketch Recognition and Style Transfer?
5 answers
Sketch recognition and style transfer are crucial in the realm of computer vision and art creation. Sketch recognition, as demonstrated by Pacheco-Portuguez et al., allows for the transformation of images into a sequence of strokes, enabling a Poppy humanoid robot to replicate sketches accurately. On the other hand, style transfer techniques, as discussed by Wang and Zhu, Warner, and Liu et al., facilitate the transfer of artistic styles from reference images to sketches or black-white images, empowering individuals with limited artistic skills to create high-resolution artwork effortlessly. By combining sketch recognition for stroke replication and style transfer for artistic enhancement, individuals can express their ideas effectively and explore diverse visual design concepts, ultimately democratizing art creation and enabling the seamless integration of various artistic styles.
What are the baby cues according to synactive theory?
4 answers
The synactive theory of development, as outlined in the Newborn Individualized Developmental Care and Assessment Program® (NIDCAP), identifies various baby cues that indicate stress or stability. These cues include movements such as flexing and extending arms and legs, hand gestures like finger splay and fisting, facial expressions like frowning and yawning, as well as tongue extensions and eye movements. Additionally, the model emphasizes the importance of interpreting these cues in response to different stimuli, ranging from non-intrusive to painful procedures, to assess the infant's well-being accurately. The theory also highlights the dynamic interplay of various subsystems within the infant's organism, such as the autonomic system, motor system, attentional-interactive system, and self-regulatory system, in understanding the individual infant.
What are the current limitation on lab assistant service robots?
5 answers
Current limitations on lab assistant service robots include the need for extensive training and integration time into laboratory environments, as highlighted in various research papers. Robots, despite their capabilities in performing tasks like pipetting with superhuman precision, require significant effort for validation and integration, often involving personnel of higher expertise levels. Additionally, while robots offer benefits like improved work efficiency and management of laboratory equipment, they may face challenges related to mobility and response to voice commands, impacting their overall effectiveness in laboratory settings. Moreover, the use of robots in laboratory automation introduces longer transport times, which can be a drawback depending on the specific requirements of the laboratory.
What are types of machine learning?
5 answers
Machine learning encompasses three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data to make predictions or classifications. Unsupervised learning, on the other hand, deals with unlabeled data to find hidden patterns or groupings. Reinforcement learning focuses on training models to make sequences of decisions by rewarding desired behaviors. These types of machine learning have diverse applications across various fields, including chemistry, healthcare, and bioinformatics. Each type of machine learning presents unique challenges and opportunities for advancing technology and improving decision-making processes in different domains.
What are the current developments for intent recognition?
5 answers
Current developments in intent recognition span various fields. In rehabilitation robotics, studies focus on recognizing movement intent through kinematic, sEMG, and EEG signals, with challenges like hysteresis and weak anti-interference abilities. In combat scenarios, a hierarchical aggregation model utilizing CNN, Bi-LSTM, and attention mechanisms achieves 88.83% accuracy in identifying air target intent. Smart homes leverage cheap sensors and ProbLog for logical and probabilistic reasoning to infer residents' intentions for tailored assistance. Additionally, an offline intent recognition system for dynamic motions shows superior performance in inferring timing and direction of human operator intent, aiding in threat evasion scenarios. These advancements highlight the diverse applications and methodologies employed in intent recognition research across different domains.
How will AI affect employment rate?
4 answers
AI's impact on employment rates is multifaceted. Studies suggest that automation driven by AI and Machine Learning could affect around 42% of the Canadian labor force, indicating a significant susceptibility to job displacement. Furthermore, research on a panel of 33 OECD countries reveals that a 10% increase in industrial robots correlates with a 0.42 point rise in the unemployment rate, with stronger effects observed in certain demographic groups, potentially contributing to labor market polarization. In China, it is estimated that over 76% of total employment will be influenced by AI in the next 20 years, emphasizing the need for public policy interventions to create new jobs that can offset those lost to automation. These findings collectively highlight the complex and varied ways in which AI could impact employment rates globally.
What is agricultural robot?
5 answers
An agricultural robot is a mechanized system designed to automate various tasks in farming, such as planting, harvesting, fertilizing, and detection, aiming to enhance efficiency and reduce labor-intensive processes. These robots integrate advanced technologies like computer vision, speech recognition, and robotics to perform specific agricultural operations accurately and autonomously. For instance, they can sow seeds, water crops, detect soil conditions, and even control the movement of the robot through Bluetooth connectivity with smartphones. By utilizing sensors, microcontrollers, and mobile applications, agricultural robots contribute to increased productivity, reduced environmental impact, and improved management of agricultural tasks, ultimately benefiting farmers by simplifying farming processes and enhancing overall agricultural output.
How to calculate joint angles from imus?
5 answers
To calculate joint angles from IMUs, various methods and technologies have been developed. Studies have shown that using Euler angle decomposition and Kalman-based filters can achieve accurate measurements for humerothoracic joints. Additionally, utilizing functional joint axis calibration tasks and kinematic constraints on gyroscope data can enhance accuracy for elbow joint measurements. Recurrent neural network (RNN) models have been proposed to estimate joint angles based on 6-axis IMUs without magnetometers, outperforming traditional Kalman filter approaches. Furthermore, sensor fusion algorithms like Madgwick and Mahony have shown promising results in estimating joint kinematics during activities of daily living, with optimizations significantly improving performance. These advancements enable real-time and high-precision joint angle measurements, even in scenarios where traditional sensors like encoders are not feasible.
Is there work in robotics on potential fields that use the dynamics of the robot?
5 answers
Yes, there is ongoing work in robotics that focuses on potential fields utilizing the dynamics of the robot. Research has explored novel approaches like using virtual fields to define desired trajectories for robot movement, enabling control over direction and speed. Additionally, studies have proposed improved artificial potential field algorithms that consider factors like gravity imbalance, local minima, and local oscillations, enhancing the robot's ability to reach targets quickly and stably. Furthermore, simulations have been conducted to plan paths for mobile robots in 2D spaces by analyzing attractive forces towards targets and repulsive forces from obstacles, effectively guiding robot motion while avoiding collisions. These advancements showcase the integration of potential fields with robot dynamics to enhance path planning and navigation strategies in robotics.
LLM system answering or giving questions?
5 answers
The Large Language Models (LLMs) discussed in the provided contexts are primarily focused on answering questions rather than generating them. These LLMs have shown impressive capabilities in various natural language processing tasks, including question answering. They have been utilized to enhance question-answering abilities by interacting with external tools. While LLMs can provide near-human levels of performance in answering questions, they may also produce incorrect answers, leading to the need for verification against external sources. The research emphasizes evaluating LLMs' abilities to utilize external tools for question answering, showcasing strengths, weaknesses, and areas for improvement. Therefore, the primary function of LLM systems, as highlighted in the contexts, is to answer questions rather than generate them.
What is the first step in the removal of an electric motor?
5 answers
The first step in the removal of an electric motor involves inspecting its functionality to determine whether it can be reused or needs to be disassembled. This initial inspection includes measuring rotation speed, current, and voltage to classify the motor. For motors classified for disassembly, the next step is to disassemble them in a robotized automatic station. The disassembly process may involve detaching components like the cable head, anchor screw, wheel screw, shell grounding wire, cooling device, wheel, bearing covers, rotor, and stator in a systematic manner to ensure part integrity and facilitate subsequent reassembly and use. By following a structured disassembling method, the removal process becomes clear, accurate, and efficient, avoiding damage to parts and ensuring ease of reassembly.