Interactive classifier system for real robot learning

Katagami, Daisuke; Yamada, Seiji

doi:10.1109/roman.2000.892505

Cited by 24 publications

(12 citation statements)

References 4 publications

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“…Since most of the time that are necessary for one time of action moreover is spent in processing time of sense system and action system of a robot, the reduction of learning trials is necessary to speedup the learning. In the Interactive Classifier System (D. Katagami et al, 2000), a human operator instructs a mobile robot while watching the information that a robot can acquire as sensor information and camera information of a robot shown on the screen top. In other words, the operator acquires information from a viewpoint of a robot instead of a viewpoint of a designer.…”

Section: Interactive Classifier System For Real Robot Learningmentioning

confidence: 99%

Robot Learning Using Learning Classifier Systems Approach

Jabin¹

2010

Robot Learning

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Section: Interactive Classifier System For Real Robot Learningmentioning

confidence: 99%

Robot Learning Using Learning Classifier Systems Approach

Jabin¹

2010

Robot Learning

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“…For instance, in autonomous robotics the environment corresponds roughly to the robot's physical surroundings and the goal of learning is to learn a certain behavior (Katagami and Yamada, 2000). In classification problems, the environment provides a set of preclassified examples.…”

Section: Learning Classifier Systemsmentioning

confidence: 99%

Real-Valued GCS Classifier System

Cielecki¹,

Unold²

2007

International Journal of Applied Mathematics and Computer Science

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Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.

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“…Previous research indicates that collaboration of a robot with a human is essential to minimize the amount of time required by a robot to accomplish a learning task (e.g., [9,[22][23][24][25][26][27][28][29][30]). Thomaz and Breazeal [31,32] describe a new RL-based approach for providing reward signals by a human which depend on both past actions and future rewards.…”

Section: Introductionmentioning

confidence: 99%

A Human-Robot Collaborative Reinforcement Learning Algorithm

Kartoun

Stern

Edan

2010

J Intell Robot Syst

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This paper presents a new reinforcement learning algorithm that enables collaborative learning between a robot and a human. The algorithm which is based on the Q(λ) approach expedites the learning process by taking advantage of human intelligence and expertise. The algorithm denoted as CQ(λ) provides the robot with self awareness to adaptively switch its collaboration level from autonomous (self performing, the robot decides which actions to take, according to its learning function) to semi-autonomous (a human advisor guides the robot and the robot combines this knowledge into its learning function). This awareness is represented by a self test of its learning performance. The approach of variable autonomy is demonstrated and evaluated using a fixed-arm robot for finding the optimal shaking policy to empty the contents of a plastic bag. A comparison between the CQ(λ) and

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Interactive classifier system for real robot learning

Cited by 24 publications

References 4 publications

Robot Learning Using Learning Classifier Systems Approach

Robot Learning Using Learning Classifier Systems Approach

Real-Valued GCS Classifier System

A Human-Robot Collaborative Reinforcement Learning Algorithm

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