[ICRA 2016] Best Conference Paper Award Finalist


Our paper on robust learning from demonstration (LfD) is the Best Conference Paper Award finalist at the IEEE International Conference on Robotics and Automation (ICRA 2016), a flagship international conference on robotics.

  • Sungjoon Choi, Kyungjae Lee, and Songhwai Oh, "Robust Learning from Demonstration Using Leveraged Gaussian Processes and Sparse-Constrained Optimization," in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May 2016. (Best Conference Paper Award Finalist)
    • Abstract: In this paper, we propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods.

About Best Conference Paper Award

  • Description: To recognize the most outstanding paper presented at the annual IEEE International Conference on Robotics and Automation (ICRA)
  • Basis for Judging: Technical merit, originality, potential impact on the field, clarity of the written paper, and quality of the oral or other presentation.

About ICRA

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.