GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1 ...
Recent advances in neural networks have introduced a new paradigm for robotic inverse kinematics. However, existing methods remain limited by insufficient feature extraction and suboptimal integration ...
Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...
Abstract: In most existing asynchronous methods, the stepsize depends on an upper bound on the delays and decreases as this bound increases. However, since the upper bound is usually unknown and large ...
To address the issues of feature mismatching and map overlap drift in simultaneous localization and mapping (SLAM) within degraded environments characterized by sparse geometric features or severe ...
Abstract: To improve the control performance of permanent magnet synchronous motor (PMSM) drive systems, this article proposes a model-free predictive current control method based on adaptive extended ...
Adaptive systems were supposed to simplify decision-making. Instead of hard-coded rules, engineers built models that could learn from data, respond to change, and improve over time. That promise still ...
This repository is publicly available on GitHub and provides full access to the source code required to reproduce all experiments reported in the manuscript. The source code has been archived on ...
Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is the ...
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