AIMC Topic: Task Performance and Analysis

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Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.

Scientific reports
Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two d...

Human-generative AI collaboration enhances task performance but undermines human's intrinsic motivation.

Scientific reports
In a series of four online experimental studies (total N = 3,562), we investigated the performance augmentation effect and psychological deprivation effect of human-generative AI (GenAI) collaboration in professional settings. Our findings consistent...

Mastering diverse control tasks through world models.

Nature
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to w...

Computer vision and tactile glove: A multimodal model in lifting task risk assessment.

Applied ergonomics
Work-related injuries from overexertion, particularly lifting, are a major concern in occupational safety. Traditional assessment tools, such as the Revised NIOSH Lifting Equation (RNLE), require significant training and practice for deployment. This...

Efficiency-Driven Adaptive Task Planning for Household Robot Based on Hierarchical Item-Environment Cognition.

IEEE transactions on cybernetics
Task planning focused on household robots represents a conventional yet complex research domain, necessitating the development of task plans that enable robots to execute unfamiliar household services. This area has garnered significant research inte...

Enhancement of long-horizon task planning via active and passive modification in large language models.

Scientific reports
This study proposes a method for generating complex and long-horizon off-line task plans using large language models (LLMs). Although several studies have been conducted in recent years on robot task planning using LLMs, the planning results tend to ...

The Impact of Human-Robot Collaboration Levels on Postural Stability During Working Tasks Performed While Standing: Experimental Study.

JMIR human factors
BACKGROUND: The integration of collaborative robots (cobots) in industrial settings has the potential to enhance worker safety and efficiency by improving postural control and reducing biomechanical risk. Understanding the specific impacts of varying...

TDAG: A multi-agent framework based on dynamic Task Decomposition and Agent Generation.

Neural networks : the official journal of the International Neural Network Society
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents often struggle during task execution due to methodological constraints,...

Performance metrics outperform physiological indicators in robotic teleoperation workload assessment.

Scientific reports
Robotics holds the potential to streamline the execution of repetitive and dangerous tasks, which are difficult or impossible for a human operator. However, in complex scenarios, such as nuclear waste management or disaster response, full automation ...

Explainable AI improves task performance in human-AI collaboration.

Scientific reports
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remain...