Publications‎ > ‎Conference Papers‎ > ‎

Towards Robust Skill Generalization: Unifying LfD and Motion Planning

posted Aug 30, 2017, 7:40 PM by Reza Ahmadzadeh
Muhammad Asif Rana, Mustafa Mukadam, S. Reza Ahmadzadeh, Sonia Chernova, Byron Boots

Reference:
Muhammad Asif Rana, Mustafa Mukadam, S. Reza Ahmadzadeh, Sonia Chernova and Byron Boots, "Towards
Robust Skill Generalization: Unifying LfD and Motion Planning", In Proc. Robotics: Science and
Systems (RSS), Workshop on (Empirically) Data-Driven Manipulation, Boston, MA, USA, pp. 1--3,
12th-16th Jul. 2017.
Bibtex Entry:
@INPROCEEDINGS{rana2017towards, TITLE={Towards Robust Skill Generalization: Unifying LfD and Motion Planning}, AUTHOR={Rana, Muhammad Asif and Mukadam, Mustafa and Ahmadzadeh, Seyed Reza and Chernova, Sonia and Boots, Byron}, BOOKTITLE={Robotics: Science and Systems ({RSS}), Workshop on (Empirically) Data-Driven Manipulation}, PAGES={1--3}, YEAR={2017}, MONTH={July}, ADDRESS={Boston, MA, USA}, }
Abstract:
We present a novel unifying approach to conventional learning from demonstration (LfD) and motion
planning using probabilistic inference for skill reproduction. We also provide a new probabilistic
skill model that requires minimal parameter tuning, and is more suited for encoding skill
constraints and performing inference in an efficient manner. Preliminary experimental results using
real-world data are presented.

PDF Preview:



Comments