Behavior Selection Method of Humanoid Robots to Perform Complex Tasks

Behavior Selection Method for Entertainment Robots Using Intelligence Operating Architecture

Advances in Intelligent Systems and Computing - Robot Intelligence Technology and Applications 2012 ◽

10.1007/978-3-642-37374-9_8 ◽

2013 ◽

pp. 75-84

Author(s):

Woo-Ri Ko ◽

Jong-Hwan Kim

Keyword(s):

Selection Method ◽

Behavior Selection

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Behavior recognition for humanoid robots using long short-term memory

International Journal of Advanced Robotic Systems ◽

10.1177/1729881416663369 ◽

2016 ◽

Vol 13 (6) ◽

pp. 172988141666336 ◽

Cited By ~ 7

Author(s):

Dickson Neoh Tze How ◽

Chu Kiong Loo ◽

Khairul Salleh Mohamed Sahari

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Building Blocks ◽

Humanoid Robots ◽

Learning From Demonstration ◽

Short Term ◽

Term Memory ◽

Complex Tasks ◽

Long Short Term Memory

Learning from demonstration plays an important role in enabling robot to acquire new behaviors from human teachers. Within learning from demonstration, robots learn new tasks by recognizing a set of preprogrammed behaviors or skills as building blocks for new, potentially more complex tasks. One important aspect in this approach is the recognition of the set of behaviors that comprises the entire task. The ability to recognize a complex task as a sequence of simple behaviors enables the robot to generalize better on more complex tasks. In this article, we propose that primitive behaviors can be taught to a robot via learning from demonstration. In our experiment, we teach the robot new behaviors by demonstrating the behaviors to the robot several times. Following that, a long short-term memory recurrent neural network is trained to recognize the behaviors. In this study, we managed to teach at least six behaviors on a NAO humanoid robot and trained a long short-term memory recurrent neural network to recognize the behaviors using the supervised learning scheme. Our result shows that long short-term memory can recognize all the taught behaviors effectively, and it is able to generalize to recognize similar types of behaviors that have not been demonstrated on the robot before. We also show that the long short-term memory is advantageous compared to other neural network frameworks in recognizing the behaviors in the presence of noise in the behaviors.

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THE CAPABILITY MAP: A TOOL TO ANALYZE ROBOT ARM WORKSPACES

International Journal of Humanoid Robotics ◽

10.1142/s021984361350031x ◽

2013 ◽

Vol 10 (04) ◽

pp. 1350031 ◽

Cited By ~ 7

Author(s):

FRANZISKA ZACHARIAS ◽

CHRISTOPH BORST ◽

SEBASTIAN WOLF ◽

GERD HIRZINGER

Keyword(s):

Industrial Robot ◽

Humanoid Robots ◽

Design Phase ◽

Robot Arm ◽

Robot System ◽

Complex Tasks ◽

Robot Arms ◽

Cooperative Tasks ◽

Robot Systems ◽

The Individual

More and more systems are developed that include several robot arms, like humanoid robots or industrial robot systems. These systems are designed for complex tasks to be solved in cooperation by the robot arms. However, the capabilities of the individual robot arms to perform given tasks or the suitability of a multi-robot system for cooperative tasks cannot be intuitively comprehended. For planning complex tasks or designing robot systems, a representation of a robot arm's workspace is needed that allows to determine from which directions objects in the workspace can be reached. In this paper, the capability map is presented. It is a representation of a robot arm's kinematic capabilities in its workspace. The capability map is used to compare existing robot arms, to support the design phase of an anthropomorphic robot arm and to enable robot workcell planning.

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