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相关概念视频

Observational Learning01:12

Observational Learning

207
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
207
Cognitive Learning01:21

Cognitive Learning

307
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
307
Purposive Learning01:22

Purposive Learning

139
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
139
Introduction to Learning01:18

Introduction to Learning

465
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
465
Associative Learning01:27

Associative Learning

434
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
434
Machines: Problem Solving II01:30

Machines: Problem Solving II

335
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
335

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相关实验视频

Updated: Jul 16, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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机器人终身对象识别在线主动持续学习

Xiangli Nie, Zhiguang Deng, Mingdong He

    IEEE transactions on neural networks and learning systems
    |September 13, 2023
    PubMed
    概括

    机器人系统现在可以在动态环境中使用在线主动持续学习 (OACL) 框架不断学习新对象. 这种方法将标签成本降至最低,并防止知识丢失,增强终身对象识别能力.

    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 机器人系统需要不断的学习来适应动态的,现实世界的环境.
    • 终身物体识别对于机器人与不断变化的环境互动和理解至关重要.
    • 现有的方法与不断变化的数据,不断变化的对象类和域移动作斗争.

    研究的目的:

    • 为机器人终身对象识别提出一个在线主动持续学习 (OACL) 框架.
    • 在动态环境中应对不断变化的阶级和领域的挑战.
    • 为了降低标签成本,同时最大限度地提高识别性能.

    主要方法:

    • 制定了在线积极学习 (OAL) 策略,考虑到样本的不确定性和多样性.
    • 提出了一种使用深度特征语义增强的在线持续学习 (OCL) 算法.
    • 实施基于损失的深度模型和重复缓冲更新,以减轻类不平衡和混乱.

    主要成果:

    • 该OACL框架使机器人能够选择信息样本进行标签.
    • 该方法有效地防止了灾难性的遗忘,并降低了记忆成本.
    • 在使用真实机器人视觉数据集的终身物体识别任务中实现了最先进的性能.

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    结论:

    • 拟议的OACL框架增强了机器人终身对象识别在非i.i.d.中. 数据流. 数据流.
    • 这种方法即使使用有限的标记样本和重复数据也有效.
    • 对于机器人来说,OACL提供了一个强大的解决方案,可以适应不断变化的环境,而不会忘记.