Results 101 to 110 of about 3,311,851 (284)
The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?
Advanced Robotics Research, EarlyView.Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley +1 more source
Exploring Semanticity for Content and Function Word Distinction in Catalan
LanguagesIn the realm of linguistics, the concept of “semanticity” was recently introduced as a novel measure designed to study linguistic networks. In a given text, semanticity is defined as the ratio of the potential number of meanings associated with a word to
Neus Català +2 more
doaj +1 more source
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
Advanced Robotics Research, EarlyView.BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source
Advanced Robotics Research, EarlyView.Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Semantic Modeling for Group Formation
, 2007 Group formation has always been a subject of interest in collaborative learning research. As it is concerned with assigning learners to the groups that maximize their benefits, computer-supported group formation can be viewed in this context as an active
Ounnas, Asma +2 more
core +1 more source
Natural Semantic Networks of the Neurorehabilitation Concept by Spanish Physiotherapists-A Qualitative Phenomenological Representational Study. [PDF]
Behav Sci (Basel), 2023 Ortega-Bastidas JA +7 more
europepmc +1 more source
Computers & Mathematics with Applications, 1992 AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and ...
openaire +1 more source
Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot
Advanced Robotics Research, EarlyView.A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini +7 more
wiley +1 more source
Multilevel Embedding and Dropout for Improved 3D Object Detection in Autonomous Driving Systems
Advanced Robotics Research, EarlyView.A plug‐in framework for RoI‐based 3D object detectors exploits previously untapped multi‐level information within RoI features. Its novel multi‐level embedding and dropout strategy enables the detection head to generate predictions at different feature levels.
Xiaohu Lu, Hayder Radha
wiley +1 more source