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Related Concept Videos

Observational Learning01:12

Observational Learning

611
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...
611
Introduction to Learning01:18

Introduction to Learning

694
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...
694
Steps in the Modeling Process01:14

Steps in the Modeling Process

459
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
459
Reinforcement01:23

Reinforcement

595
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
595
Associative Learning01:27

Associative Learning

844
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...
844
Purposive Learning01:22

Purposive Learning

285
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...
285

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning.

Jiang Hua1, Liangcai Zeng1, Gongfa Li1

  • 1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This survey explores intelligent robots capable of autonomous learning and decision-making. Recent advances in deep reinforcement learning enable robots to perform complex manipulation tasks, moving beyond structured environments.

Keywords:
adaptive and robust controldeep reinforcement learningdexterous manipulationimitation learningtransfer learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Robotic manipulators are limited to simple tasks in structured environments.
  • Dexterous manipulation is key to achieving artificial intelligence in robots.

Purpose of the Study:

  • To provide a state-of-the-art survey on intelligent robots with autonomous decision-making and learning capabilities.
  • To analyze the evolution of robot control from automatic systems to AI-driven methods.

Main Methods:

  • Review of research in automatic control and mechanical hardware.
  • Analysis of advancements in adaptive and robust robot control.
  • Detailed discussion of deep learning, reinforcement learning, deep reinforcement learning, imitation learning, and transfer learning in robot control.

Main Results:

  • Breakthroughs in automatic control and mechanics initially advanced robot capabilities.
  • Artificial intelligence, particularly deep and reinforcement learning, has enabled robots to tackle complex tasks.
  • Latest research highlights the potential of deep reinforcement learning, imitation learning, and transfer learning for sophisticated robot control.

Conclusions:

  • Intelligent robots are increasingly capable of autonomous decision-making and learning.
  • Deep reinforcement learning and related techniques are crucial for advancing robot manipulation.
  • Future research should address challenges in complex task execution and enhanced robot intelligence.