scholarly journals Humanoid robot learning and game playing using PC-based vision

2003 ◽  
Author(s):  
D.C. Bentivegna ◽  
A. Ude ◽  
C.G. Atkeson ◽  
G. Cheng
Keyword(s):  
Humanoid Robot ◽  
Robot Learning ◽  
Game Playing

scholarly journals LEARNING TO ACT FROM OBSERVATION AND PRACTICE

2004 ◽  
Vol 01 (04) ◽  
pp. 585-611 ◽  
Author(s):  
DARRIN C. BENTIVEGNA ◽  
CHRISTOPHER G. ATKESON ◽  
ALEŠ UDE ◽  
GORDON CHENG
Keyword(s):  
Domain Knowledge ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Robot Learning ◽  
Learning Problems

We present a method for humanoid robots to quickly learn new dynamic tasks from observing others and from practice. Ways in which the robot can adapt to initial and also changing conditions are described. Agents are given domain knowledge in the form of task primitives. A key element of our approach is to break learning problems up into as many simple learning problems as possible. We present a case study of a humanoid robot learning to play air hockey.

A Humanoid Robot Learning Audiovisual Classification By Active Exploration

2021 ◽  
Author(s):  
Glareh Mir ◽  
Matthias Kerzel ◽  
Erik Strahl ◽  
Stefan Wermter
Keyword(s):  
Humanoid Robot ◽  
Robot Learning ◽  
Active Exploration

scholarly journals Acquisition and Utilization of Mental Imagery Capability in Robotic Action Sequencing Tasks

2020 ◽  
Vol 13 (2) ◽  
pp. 196-207
Author(s):  
Kristsana Seepanomwan
Keyword(s):  
Mental Imagery ◽  
Tool Use ◽  
Humanoid Robot ◽  
Computational Models ◽  
Robot Learning ◽  
Service Robot ◽  
Human Infants ◽  
Action Sequencing ◽  
Robotic Application ◽  
Robotic Applications

This work presents a series of neurorobotic models underlying learning in robots. It demonstrates the way in which, during sensorimotor exploration, robots do not just gain knowledge about how to form movement primitives but also obtain a mental imagery capability. Mental imagery plays a key role in these computational models by accelerating learning processes of action sequencing tasks. The first experiment involves permitting a humanoid robot to learn how to retrieve an out-of-reach object using a provided tool. This experiment explores a phenomenon on tool use development found in human infants. In addition, it tests two hypotheses on tool use development. The second experiment extends the domain of robot learning by targeting a useful robotic application. It drives a service robot to learn to acquire knowledge of how to manipulate perceived objects based on the objects’ information and a goal from users. By means of planning, learning the sequence of actions in mind, the robots are able to learn by examining actions’ outcome without really performing actions. This allows the second model to completely exclude parts of overt movements from the training loop. The results confirm that two types of robots can complete their given tasks in a reasonable period of time. The proposed models would benefit robotic applications in terms of online learning.

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