Articles tagged with Robotic Learning
Researchers at UTIA and UT Knoxville create an automated sensor network to monitor compost piles, reducing labor costs and improving regulatory compliance. The new system uses battery-free sensors and machine learning algorithms to analyze temperature and moisture variations, enabling data-driven decision making.
Researchers propose a novel approach to AI hardware design by integrating neuromorphic systems and compute-in-memory techniques to overcome the limitations of modern computing hardware. This could lead to more efficient data center energy use and enable real-time intelligence in compact, power-constrained systems.
Researchers propose integrating processing capability within memory units to reduce energy consumption and latency in AI applications. Inspired by the brain's efficient processing mechanisms, spiking neural networks (SNNs) can respond to irregular events and store information in the same place.
Embodied learning enables robots to adapt to new situations through physical interactions and feedback loops. This survey summarizes the latest research in object-centric robotic manipulation, covering embodied perceptual learning, policy learning, and task-oriented learning.
Grade school children learn robotics through performance-based learning, exploring science, tech, engineering, arts, and math. The expanded curriculum will introduce ethical considerations, such as fairness, privacy, and bias in technology.
A new robotic slip-prevention method has been developed to improve robots' grip and handling of fragile or slippery objects. This bio-inspired approach allows robots to predict when an object might slip and adapt their movements in real-time, outperforming traditional strategies.
Researchers developed CoSyn, a new approach to train open-source models using AI-generated scientific figures and charts. The resulting dataset, CoSyn-400K, includes over 400,000 synthetic images and 2.7 million sets of corresponding instructions. CoSyn-trained models match or outperform proprietary peers in various benchmark tests.
Researchers found that silkworm moths scan pheromone sources by rotating their bodies while fanning, helping them detect odor plumes. The study offers valuable design principles for aerial robots, enabling more efficient search and localization of targets.
Researchers used an AI-assisted application to help people write cartoon captions for The New Yorker Cartoon Caption Contest. The tool analyzed incongruity and generated suggestions, resulting in jokes rated 30% funnier than those written without assistance.
A Lancaster University academic argues that AI and algorithms contribute to polarization, radicalism, and political violence, posing a threat to national security. The paper examines how AI has been securitized throughout its history, highlighting the need for better understanding and management of its risks.
A team of researchers from the Institute of Industrial Science, The University of Tokyo, used a mathematical model to examine the implications of intergenerational learning. They found that learning accelerated the evolutionary process, which may assist in designing more efficient hybrid algorithms.
Researchers from The University of Tokyo Institute of Industrial Science have found that drones can be used as communication bases with underwater robotic devices (AUVs) for ocean surveys. UAVs offer high-speed observations, mobility, and resistance to ocean currents, making them suitable candidates for this application.