Abstract
Robot learning from demonstrations requires the robot to learn and adapt movements to new situations, often characterized by position and orientation of objects or landmarks in the robot's environment. In the task-parameterized Gaussian mixture model framework, the movements are considered to be modulated with respect to a set of candidate frames of reference (coordinate systems) attached to a set of objects in the robot workspace. Following a similar approach, this paper addresses the problem of having missing candidate frames during the demonstrations and reproductions, which can happen in various situations such as visual occlusion, sensor unavailability, or tasks with a variable number of descriptive features. We study this problem with a dust sweeping task in which the robot requires to consider a variable amount of dust areas to clean for each reproduction trial.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Conference on Robotics and Automation |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3309-3314 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - Sep 22 2014 |
| Externally published | Yes |
| Event | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014 |
Other
| Other | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 5/31/14 → 6/7/14 |
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ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Learning from demonstrations with partially observable task parameters. / Alizadeh, Tohid; Calinon, Sylvain; Caldwell, Darwin G.
Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3309-3314 6907335.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning from demonstrations with partially observable task parameters
AU - Alizadeh, Tohid
AU - Calinon, Sylvain
AU - Caldwell, Darwin G.
PY - 2014/9/22
Y1 - 2014/9/22
N2 - Robot learning from demonstrations requires the robot to learn and adapt movements to new situations, often characterized by position and orientation of objects or landmarks in the robot's environment. In the task-parameterized Gaussian mixture model framework, the movements are considered to be modulated with respect to a set of candidate frames of reference (coordinate systems) attached to a set of objects in the robot workspace. Following a similar approach, this paper addresses the problem of having missing candidate frames during the demonstrations and reproductions, which can happen in various situations such as visual occlusion, sensor unavailability, or tasks with a variable number of descriptive features. We study this problem with a dust sweeping task in which the robot requires to consider a variable amount of dust areas to clean for each reproduction trial.
AB - Robot learning from demonstrations requires the robot to learn and adapt movements to new situations, often characterized by position and orientation of objects or landmarks in the robot's environment. In the task-parameterized Gaussian mixture model framework, the movements are considered to be modulated with respect to a set of candidate frames of reference (coordinate systems) attached to a set of objects in the robot workspace. Following a similar approach, this paper addresses the problem of having missing candidate frames during the demonstrations and reproductions, which can happen in various situations such as visual occlusion, sensor unavailability, or tasks with a variable number of descriptive features. We study this problem with a dust sweeping task in which the robot requires to consider a variable amount of dust areas to clean for each reproduction trial.
UR - http://www.scopus.com/inward/record.url?scp=84929191868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929191868&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2014.6907335
DO - 10.1109/ICRA.2014.6907335
M3 - Conference contribution
SP - 3309
EP - 3314
BT - Proceedings - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
ER -