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Observational Learning01:12

Observational Learning

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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...
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Related Experiment Video

Updated: Dec 4, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Path Planning for Multi-Arm Manipulators Using Deep Reinforcement Learning: Soft Actor-Critic with Hindsight

Evan Prianto1, MyeongSeop Kim1, Jae-Han Park2

  • 1Department of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul 01811, Korea.

Sensors (Basel, Switzerland)
|October 22, 2020
PubMed
Summary

This study introduces a new Soft Actor-Critic (SAC) algorithm for multi-arm manipulator path planning. The method enhances exploration and sample efficiency, outperforming existing approaches in simulations and experiments.

Keywords:
Hindsight Experience Replay (HER)Soft Actor-Critic (SAC)collision avoidancemulti-arm manipulatorspath planningreinforcement learning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Path planning for multi-arm manipulators presents a high-dimensional challenge, hindering efficient and rapid trajectory generation.
  • Deep reinforcement learning methods struggle with effective exploration in these complex, high-dimensional spaces.

Purpose of the Study:

  • To develop an effective and efficient path planning algorithm for multi-arm manipulators using deep reinforcement learning.
  • To address the challenges of high dimensionality and sample inefficiency in manipulator path planning.

Main Methods:

  • Proposed a novel path planning algorithm based on Soft Actor-Critic (SAC), known for its exploration capabilities.
  • Incorporated Hindsight Experience Replay (HER) to improve sample efficiency.
  • Utilized configuration space augmentation to handle complex multi-arm configurations.

Main Results:

  • The proposed SAC-based algorithm demonstrated superior performance in path planning tasks.
  • Simulation and experimental results validated the effectiveness of the new approach.
  • The method showed significant improvements compared to existing path planning techniques.

Conclusions:

  • The developed SAC-based path planning algorithm offers an effective solution for multi-arm manipulator challenges.
  • The integration of HER and configuration space augmentation enhances both exploration and sample efficiency.
  • This work advances the state-of-the-art in robotic path planning.