Imitation learning and attentional supervision of dual-arm structured tasks

2017 ◽  
Author(s):  
R. Caccavale ◽  
M. Saveriano ◽  
G. A. Fontanelli ◽  
F. Ficuciello ◽  
D. Lee ◽  
...  
Keyword(s):  
Imitation Learning ◽  
Dual Arm

scholarly journals Robot Imitation Learning of Social Gestures with Self-Collision Avoidance Using a 3D Sensor

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2355 ◽  
Author(s):  
Tan Zhang ◽  
Wing-Yue Louie ◽  
Goldie Nejat ◽  
Beno Benhabib
Keyword(s):  
Social Communication ◽  
Learning System ◽  
Imitation Learning ◽  
High Dimensionality ◽  
Human Behaviors ◽  
Bounding Volumes ◽  
Communication Modes ◽  
Robot Configuration ◽  
Kinematical Approach ◽  
Dual Arm

To effectively interact with people, social robots need to perceive human behaviors and in turn display their own behaviors using social communication modes such as gestures. The modeling of gestures can be difficult due to the high dimensionality of the robot configuration space. Imitation learning can be used to teach a robot to implement multi-jointed arm gestures by directly observing a human teacher’s arm movements (for example, using a non-contact 3D sensor) and then mapping these movements onto the robot arms. In this paper, we present a novel imitation learning system with robot self-collision awareness and avoidance. The proposed method uses a kinematical approach with bounding volumes to detect and avoid collisions with the robot itself while performing gesticulations. We conducted experiments with a dual arm social robot and a 3D sensor to determine the effectiveness of our imitation system in being able to mimic gestures while avoiding self-collisions.

IMITATION LEARNING OF DUAL-ARM MANIPULATION TASKS IN HUMANOID ROBOTS

2008 ◽  
Vol 05 (02) ◽  
pp. 183-202 ◽  
Author(s):  
T. ASFOUR ◽  
P. AZAD ◽  
F. GYARFAS ◽  
R. DILLMANN
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Arm Movements ◽  
Humanoid Robots ◽  
Imitation Learning ◽  
Upper Body ◽  
Key Points ◽  
Characteristic Features ◽  
Almost All ◽  
Dual Arm

In this paper, we present an approach to imitation learning of arm movements in humanoid robots. Continuous hidden Markov models (HMMs) are used to generalize movements demonstrated to a robot multiple times. Characteristic features of the perceived movement, so-called key points, are detected in a preprocessing stage and used to train the HMMs. For the reproduction of a perceived movement, key points that are common to all (or almost all) demonstrations, so-called common key points, are used. These common key points are determined by comparing the HMM state sequences and selecting only those states that appear in every sequence. We also show how the HMM can be used to detect temporal dependencies between the two arms in dual-arm tasks. Experiments reported in this paper have been performed using a kinematics model of the human upper body to simulate the reproduction of arm movements and the generation of natural-looking joint configurations from perceived hand paths. Results are presented and discussed.

Study on Trajectory Planning of Dual-arm Space Robot Keeping the Base Stabilized

2014 ◽  
Vol 39 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Wen-Fu XU ◽  
Xue-Qian WANG ◽  
Qiang XUE ◽  
Bin LIANG
Keyword(s):  
Trajectory Planning ◽  
Space Robot ◽  
Dual Arm

Robot Imitation Learning of High-Level Planning Information

2005 ◽  
Author(s):  
Frderick L. Crabbe ◽  
Rebecca Hwa
Keyword(s):  
Imitation Learning ◽  
High Level
Sign in / Sign up

Export Citation Format

Share Document