Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion
This paper introduces an imitation system based on the similarity of the replaying motions of robots with the sequential poses of a demonstrator. The system is composed of three modules-key pose elicitation, real robot balance control, and memorization for motion replay. The elicitation of key poses...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2017-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8014447/ |
| _version_ | 1828966544422469632 |
|---|---|
| author | Jin-Ling Lin Kao-Shing Hwang |
| author_facet | Jin-Ling Lin Kao-Shing Hwang |
| author_sort | Jin-Ling Lin |
| collection | DOAJ |
| description | This paper introduces an imitation system based on the similarity of the replaying motions of robots with the sequential poses of a demonstrator. The system is composed of three modules-key pose elicitation, real robot balance control, and memorization for motion replay. The elicitation of key poses drives the balance learning and motion replay of the robot. Dissimilarity values associated with the defined spatiotemporal function of simultaneous joint motion are used to analyze the degree of similarity. To overcome the difference in mechanical structures and kinematics, such as the number of joints between robots and human demonstrators, the key poses extracted from the motions of demonstrators are modified by a Q-Learning process that considers the kinematic constraints and maintains the balance of the robot while executing imitation. The rewards were designed not only to encourage a robot to execute as many consecutive poses as possible, but also to guide the robot to maintain its balance even though the biped lacks information on the ankle joint. These modified key poses are stored in databases for replaying or composing new motions in an ordered sequence. The experimental results demonstrate that a robot could adjust the poses, mapped from the movements of the demonstrator, to its static stable states, thereby imitating human motions by self-learning. |
| first_indexed | 2024-12-14T11:33:02Z |
| format | Article |
| id | doaj.art-1e4d481b71c84f508a09f3546bd940b1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:33:02Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1e4d481b71c84f508a09f3546bd940b12022-12-21T23:03:11ZengIEEEIEEE Access2169-35362017-01-015175341754210.1109/ACCESS.2017.27430688014447Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of MotionJin-Ling Lin0Kao-Shing Hwang1https://orcid.org/0000-0003-4432-8801Department of Information Management, Shih Hsin University, Taipei, TaiwanDepartment of Electrical Engineering, National Sun Yat-sen University, Kaohisung, TaiwanThis paper introduces an imitation system based on the similarity of the replaying motions of robots with the sequential poses of a demonstrator. The system is composed of three modules-key pose elicitation, real robot balance control, and memorization for motion replay. The elicitation of key poses drives the balance learning and motion replay of the robot. Dissimilarity values associated with the defined spatiotemporal function of simultaneous joint motion are used to analyze the degree of similarity. To overcome the difference in mechanical structures and kinematics, such as the number of joints between robots and human demonstrators, the key poses extracted from the motions of demonstrators are modified by a Q-Learning process that considers the kinematic constraints and maintains the balance of the robot while executing imitation. The rewards were designed not only to encourage a robot to execute as many consecutive poses as possible, but also to guide the robot to maintain its balance even though the biped lacks information on the ankle joint. These modified key poses are stored in databases for replaying or composing new motions in an ordered sequence. The experimental results demonstrate that a robot could adjust the poses, mapped from the movements of the demonstrator, to its static stable states, thereby imitating human motions by self-learning.https://ieeexplore.ieee.org/document/8014447/Motion imitationmotion replayhumanoid robotreinforcement learning |
| spellingShingle | Jin-Ling Lin Kao-Shing Hwang Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion IEEE Access Motion imitation motion replay humanoid robot reinforcement learning |
| title | Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion |
| title_full | Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion |
| title_fullStr | Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion |
| title_full_unstemmed | Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion |
| title_short | Balancing and Reconstruction of Segmented Postures for Humanoid Robots in Imitation of Motion |
| title_sort | balancing and reconstruction of segmented postures for humanoid robots in imitation of motion |
| topic | Motion imitation motion replay humanoid robot reinforcement learning |
| url | https://ieeexplore.ieee.org/document/8014447/ |
| work_keys_str_mv | AT jinlinglin balancingandreconstructionofsegmentedposturesforhumanoidrobotsinimitationofmotion AT kaoshinghwang balancingandreconstructionofsegmentedposturesforhumanoidrobotsinimitationofmotion |