Results 101 to 110 of about 28,763 (261)

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition

IEEE Access
Few-shot action recognition aims to train a model to classify actions in videos using only a few examples, known as “shots,” per action class.
Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee   +5 more
doaj   +1 more source

Prototypical Networks for Few-shot Learning

CoRR, 2017
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of ...
Jake Snell, Kevin Swersky, Richard S. Zemel   +2 more
openaire   +3 more sources

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

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, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

Tibetan Few‐Shot Learning Model With Deep Contextualised Two‐Level Word Embeddings

CAAI Transactions on Intelligence Technology
Few‐shot learning is the task of identifying new text categories from a limited set of training examples. The two key challenges in few‐shot learning are insufficient understanding of new samples and imperfect modelling.
Ziyue Zhang, Yongbin Yu, Xiangxiang Wang, Xiao Feng, Yuze Li, Jiarun Shen, Dorje Tashi, Jin Zhang, Lobsang Yeshi, Lei Li, Nyima Tashi, Jingye Cai   +11 more
doaj   +1 more source

Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications

Journal of Agricultural Sciences
Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance.
Sezai Ercisli, Jingbin Li, Kangle Song, Yi Wang, Huting Wang, Yichen Yuan, Jing Nie, Yang Li   +7 more
doaj   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

ChicGrasp: Imitation‐Learning‐Based Customized Dual‐Jaw Gripper Control for Manipulation of Delicate, Irregular Bio‐Products

Advanced Robotics Research, EarlyView.
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar, Zhengtong Xu, Siavash Mahmoudi, Pouya Sohrabipour, Chaitanya Pallerla, Yu She, Wan Shou, Philip Glen Crandall, Dongyi Wang   +8 more
wiley   +1 more source