Results 21 to 30 of about 42,816 (266)
Robot Skill Learning Based on Dynamic Motion Primitives and Adaptive Control
Shanghai Jiaotong Daxue xuebao, 2023 A novel robot skill learning method using dynamic movement primitive (DMP) and adaptive control is proposed. The existing DMP method learns actions from a single teaching trajectory, and its Gaussian basis function distribution mode is fixed, which is ...
ZHANG Wenan, GAO Weizhan, LIU Andong
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International Journal of Advanced Robotic Systems, 2019 In this article, we analyze the singularities of six-degree-of-freedom anthropomorphic manipulators and design a singularity handling algorithm that can smoothly go through singular regions.
Zhi-Hao Kang +2 more
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Learning Trajectory Distributions for Assisted Teleoperation and Path Planning
Frontiers in Robotics and AI, 2019 Several approaches have been proposed to assist humans in co-manipulation and teleoperation tasks given demonstrated trajectories. However, these approaches are not applicable when the demonstrations are suboptimal or when the generalization capabilities
Marco Ewerton +9 more
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Research on Self-Recovery Control Algorithm of Quadruped Robot Fall Based on Reinforcement Learning
Actuators, 2023 When a quadruped robot is climbing stairs, due to unexpected factors, such as the size of the differing from the international standard or the stairs being wet and slippery, it may suddenly fall down.
Guichen Zhang +4 more
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Autonomous learning in robotics
, 2023 Recent advances in artificial neural networks enabled the quick development of new learning algorithms, which, among other things, pave the way to novel robotic applications. Traditionally, robots are programmed by human experts so as to accomplish pre-defined tasks.
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BiomimeticsIn recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring.
Mingen Wang +6 more
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Patching interpretable And‐Or‐Graph knowledge representation using augmented reality
Applied AI Letters, 2021 We present a novel augmented reality (AR) interface to provide effective means to diagnose a robot's erroneous behaviors, endow it with new skills, and patch its knowledge structure represented by an And‐Or‐Graph (AOG).
Hangxin Liu, Yixin Zhu, Song‐Chun Zhu
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Modeling Active Learning in a Robot Collective
Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020 In this research, we model an active learning method on real robots that can visually learn from each other. For this purpose, we initially design an experiment scenario in which a teacher robot presents a simple classification task to a learner robot ...
Mehmet Dinçer Erbaş
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Journal of Robotics, 2012 This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model.
Huan Tan, Qian Du 0002, Na Wu
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Graph Learning in Robotics: A Survey
IEEE Access, 2023 Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics ...
Francesca Pistilli, Giuseppe Averta
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