Abstract
In this paper we address the problem of interactive robot movement adaptation under various environmental constraints. A common approach is to adopt motion primitives to generate target motions from demonstrations. However, their generalization capability is weak for novel environments. Additionally, traditional motion generation methods do not consider versatile constraints from different users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt the movement of robot end-effectors for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with different constraints. The framework also considers user feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. Experiments on a humanoid platform validate the effectiveness of our approach.
| Original language | English (US) |
|---|---|
| Title of host publication | Humanoids 2016 - IEEE-RAS International Conference on Humanoid Robots |
| Publisher | IEEE Computer Society |
| Pages | 372-378 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509047185 |
| DOIs | |
| State | Published - Dec 30 2016 |
| Event | 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016 - Cancun, Mexico Duration: Nov 15 2016 → Nov 17 2016 |
Other
| Other | 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 11/15/16 → 11/17/16 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Hardware and Architecture
- Human-Computer Interaction
- Electrical and Electronic Engineering
Cite this
Co-active learning to adapt humanoid movement for manipulation. / Mao, Ren; Baras, John S.; Yang, Yezhou; Fermüller, Cornelia.
Humanoids 2016 - IEEE-RAS International Conference on Humanoid Robots. IEEE Computer Society, 2016. p. 372-378 7803303.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Co-active learning to adapt humanoid movement for manipulation
AU - Mao, Ren
AU - Baras, John S.
AU - Yang, Yezhou
AU - Fermüller, Cornelia
PY - 2016/12/30
Y1 - 2016/12/30
N2 - In this paper we address the problem of interactive robot movement adaptation under various environmental constraints. A common approach is to adopt motion primitives to generate target motions from demonstrations. However, their generalization capability is weak for novel environments. Additionally, traditional motion generation methods do not consider versatile constraints from different users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt the movement of robot end-effectors for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with different constraints. The framework also considers user feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. Experiments on a humanoid platform validate the effectiveness of our approach.
AB - In this paper we address the problem of interactive robot movement adaptation under various environmental constraints. A common approach is to adopt motion primitives to generate target motions from demonstrations. However, their generalization capability is weak for novel environments. Additionally, traditional motion generation methods do not consider versatile constraints from different users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt the movement of robot end-effectors for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with different constraints. The framework also considers user feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. Experiments on a humanoid platform validate the effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=85010203554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85010203554&partnerID=8YFLogxK
U2 - 10.1109/HUMANOIDS.2016.7803303
DO - 10.1109/HUMANOIDS.2016.7803303
M3 - Conference contribution
AN - SCOPUS:85010203554
SP - 372
EP - 378
BT - Humanoids 2016 - IEEE-RAS International Conference on Humanoid Robots
PB - IEEE Computer Society
ER -