Affect-driven Robot Behavior Learning System using EEG Signals for Less Negative Feelings and More Positive Outcomes

2021 ◽  
Author(s):  
Byung Hyung Kim ◽  
Ji Ho Kwak ◽  
Minuk Kim ◽  
Sungho Jo
Keyword(s):  
Learning System ◽  
Positive Outcomes ◽  
Eeg Signals ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Negative Feelings

Drawing Light(ning) from the Clouds of Social Aggression

2012 ◽  
Vol 1 (3) ◽  
pp. 347-383 ◽  
Author(s):  
Erin K. Willer
Keyword(s):  
Middle School ◽  
Social Aggression ◽  
Visual Narrative ◽  
Positive Outcomes ◽  
Master Narrative ◽  
Middle School Girls ◽  
Making Sense ◽  
School Girls ◽  
Negative Feelings ◽  
Insight Into

The master narrative about social aggression is that it is devastating for girls. Absent from the narrative, however, are girls' voices and a consideration of the positive benefits that targets might incur. Girls' stories of social aggression can be hard to communicate, as adolescents experience challenges making sense of emotionally difficult events. Using Burke's dramaturgical perspective and visual narrative metaphor method, the present study provided girls with a means of purification or a way of identifying both the devastating and redeeming nature of social aggression, including a sequential move from pollution to redemption. Forty-two middle school girls drew and orally described metaphors representing their negative feelings and positive outcomes associated with an experience of social aggression. The analysis revealed four categories of pollution metaphors and four categories of redemption metaphors, as well as five discourse structures that provided insight into how participants constructed their pollution and redemption narratives.

scholarly journals A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3672 ◽  
Author(s):  
Chao Lu ◽  
Jianwei Gong ◽  
Chen Lv ◽  
Xin Chen ◽  
Dongpu Cao ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Speed Control ◽  
Autonomous Driving ◽  
Learning System ◽  
Superior Performance ◽  
Level Control ◽  
Cruise Control ◽  
Behavior Learning ◽  
Main Component

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.

Experimental Study on Behavior Acquisition of Mobile Robot by Deep Q-Network

2017 ◽  
Vol 21 (5) ◽  
pp. 840-848 ◽  
Author(s):  
Hikaru Sasaki ◽  
Tadashi Horiuchi ◽  
Satoru Kato ◽  
◽  
◽  
...  
Keyword(s):  
Mobile Robot ◽  
Visual Information ◽  
Profit Sharing ◽  
Simulation Software ◽  
Learning Performance ◽  
Simulation Environment ◽  
Q Learning ◽  
Robot Behavior ◽  
Target Network ◽  
Behavior Learning

Deep Q-network (DQN) is one of the most famous methods of deep reinforcement learning. DQN approximates the action-value function using Convolutional Neural Network (CNN) and updates it using Q-learning. In this study, we applied DQN to robot behavior learning in a simulation environment. We constructed the simulation environment for a two-wheeled mobile robot using the robot simulation software, Webots. The mobile robot acquired good behavior such as avoiding walls and moving along a center line by learning from high-dimensional visual information supplied as input data. We propose a method that reuses the best target network so far when the learning performance suddenly falls. Moreover, we incorporate Profit Sharing method into DQN in order to accelerate learning. Through the simulation experiment, we confirmed that our method is effective.

KSU-IMR Mobile Robot Navigation Maps Building and Learning

2015 ◽  
Vol 789-790 ◽  
pp. 717-722
Author(s):  
Ebrahim Mattar ◽  
K. Al Mutib ◽  
M. AlSulaiman ◽  
Hedjar Ramdane
Keyword(s):  
Mobile Robot ◽  
Dimensionality Reduction ◽  
Robot Navigation ◽  
Mobile Robot Navigation ◽  
Research Outcomes ◽  
Neuro Fuzzy ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Mobile Maps

It is essential to learn a robot navigation environment. We describe research outcomes for KSU-IMR mapping and intelligence. This is for navigating and robot behavior learning. The mobile maps learning and intelligence was based on hybrid paradigms and AI functionaries. Intelligence was based on ANN-PCA for dimensionality reduction, and Neuro-Fuzzy architecture.

Scenario and context specific visual robot behavior learning

2011 ◽  
Author(s):  
Krishna Kumar Narayanan ◽  
Luis Felipe Posada ◽  
Frank Hoffmann ◽  
Torsten Bertram
Keyword(s):  
Robot Behavior ◽  
Behavior Learning ◽  
Context Specific

AN IMPROVED INTERNAL MODEL FOR SWARM FORMATION AND ADAPTIVE SWARM BEHAVIOR ACQUISITION

2009 ◽  
Vol 18 (08) ◽  
pp. 1517-1531 ◽  
Author(s):  
TAKASHI KUREMOTO ◽  
YUKI YAMANO ◽  
MASANAO OBAYASHI ◽  
KUNIKAZU KOBAYASHI
Keyword(s):  
Internal Model ◽  
Learning Algorithm ◽  
Learning System ◽  
Neuro Fuzzy ◽  
Swarm Behavior ◽  
Markov Decision ◽  
Behavior Learning ◽  
Improved Learning ◽  
Partially Observable ◽  
Fuzzy Learning

To form a swarm and acquire swarm behaviors adaptive to the environment, we proposed a neuro-fuzzy learning system as a common internal model of each individual recently. The proposed swarm behavior learning system showed its efficient accomplishment in the simulation experiments of goal-exploration problems. However, the input information observed from the environment in our conventional methods was given by coordinate spaces (discrete or continuous) which were difficult to be obtained in the real world by the individuals. This paper intends to improve our previous neuro-fuzzy learning system to deal with the local-limited observation, i.e., usually being a Partially Observable Markov Decision Process (POMDP), by adopting eligibility traces and balancing trade-off between exploration and exploitation to the conventional learning algorithm. Simulations of goal-oriented problems for swarm learning were executed and the results showed the effectiveness of the improved learning system.

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