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Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Mean free path and Mean free time

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Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."

Frustration and Conflict: Avoidance-Avoidance, Double-Approach Avoidance

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Avoidance-avoidance conflict refers to a psychological situation where a person must choose between two or more unpleasant alternatives. These conflicts are particularly stressful because neither option is desirable. This dilemma is often expressed in sayings like "caught between a rock and a hard place" or "between the devil and the deep blue sea." For instance, individuals who fear dental procedures may find themselves torn between enduring a painful toothache or facing the...

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The pectoralis major has two origins. Its clavicular head originates on the medial half of the clavicle. In contrast, the sternocostal head originates on the costal cartilages of ribs 1-6, the sternum, and the aponeurosis of the external oblique of the...

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関連する実験動画

Updated: Feb 8, 2026

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Published on: October 3, 2020

ロボットアームの階層的強化学習と経路最適化を用いた適応的障害物回避

Chun Ma¹, Tiecheng Su¹, Yuede Pan¹

¹College of Mechanical Engineering, Guangxi University, Nanning, China.

Annals of the New York Academy of Sciences

|February 6, 2026

まとめ

この研究では、プロキシマルポリシー最適化（PPO）とラピッド探索ランダムツリー（RRT*）を組み合わせた新しいロボットアーム動作計画手法を導入し、動的障害物回避において87.6%の成功率を達成しました。

キーワード:

カリキュラム学習（CL）動的障害物回避動作計画プロキシマルポリシー最適化（PPO）ラピッド探索ランダムツリー（RRT*）

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関連する実験動画

Last Updated: Feb 8, 2026

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

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Published on: October 3, 2020

Adaptation of a Haptic Robot in a 3T fMRI

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Published on: October 4, 2011

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

科学分野:

ロボット工学
人工知能
動作計画

背景:

動的障害物回避は、ロボットアームの動作計画における重要な課題です。
従来のメソッドは、非構造化環境における適応的決定と正確な軌道生成に苦労しています。

研究の目的:

動的障害物回避のための新しい階層的動作計画フレームワークを提示すること。
複雑で非構造化された環境におけるロボットアームの能力を強化すること。

主な方法:

プロキシマルポリシー最適化（PPO）とラピッド探索ランダムツリー（RRT*）を組み合わせた階層的フレームワーク。
段階的に困難なシナリオでPPOをトレーニングするためのカリキュラム学習パラダイム。
ステップ効率と人工ポテンシャルフィールドの原則を使用して、探索と活用をバランスさせるマルチ目的報酬関数。

主要な成果:

動的障害物シナリオで87.6%の成功率を達成しました。
スタンドアロンのPPOおよび既存のハイブリッド強化学習アプローチを上回りました。
効果的なグローバル障害物回避戦略（PPO）と洗練されたローカルトラジェクトリ（RRT*）を実証しました。

結論:

提案されたフレームワークは、ロボットアームにおける動的障害物回避の実用的なソリューションを提供します。
この方法は、高次元自律操作タスクに広く適用可能です。
階層的アプローチは、グローバル戦略学習と精密な運動制御を効果的にバランスさせます。