Results 91 to 100 of about 305,521 (291)
Building a semantic map: top-down versus bottom-up approaches
Linguistic Discovery, 2010 This paper contrasts two methods for constructing semantic maps: the top-down model and the bottom-up model. It is argued that the bottom-up approach can be illuminating in solving long-standing issues.
Ferdinand de Haan
doaj +1 more source
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
, 2017 Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself.
Guu, Kelvin +3 more
core +1 more source
Semantic Maps of Twitter Conversations
, 2017 Twitter is an irreplaceable source of data for opinion mining, emergency communications, or fact sharing, whose readability is severely limited by the sheer volume of tweets published every day. A method to represent and synthesize the information content of conversations on Twitter in form of semantic maps, from which the main topics and the main ...
Ciaramella, Angelo +2 more
openaire +1 more source
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
Self-Supervised Audio-Visual Co-Segmentation
, 2019 Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data.
Gan, Chuang +4 more
core +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
Linguistic TypologyThis investigation is a large-scale comparative corpus study of the oppositive contrast domain (also called “semantic opposition”) based on parallel texts.
Wälchli Bernhard
doaj +1 more source
3D Semantic Map Reconstruction for Orchard Environments Using Multi-Sensor Fusion
AgricultureSemantic point cloud maps play a pivotal role in smart agriculture. They provide not only core three-dimensional data for orchard management but also empower robots with environmental perception, enabling safer and more efficient navigation and planning.
Quanchao Wang +4 more
doaj +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