Robot Learning Laboratory
About the Laboratory & Research Area
Robot learning is practical machine learning methods which are applied to physical systems, such as robots.
In robot learning, the difficulties lie in the study of influence of robot action on the environment and learning with sensory values which has causal relationship with robot’s action.
Robot learning can be considered as a new machine learning technology to overcome the difficulties in applying machine learning to physical systems.
In RLLAB (Robot Learning Laboratory), we study theory and robot learning applications including robotics, computer vision and machine learning.
In robot learning, the difficulties lie in the study of influence of robot action on the environment and learning with sensory values which has causal relationship with robot’s action.
Robot learning can be considered as a new machine learning technology to overcome the difficulties in applying machine learning to physical systems.
In RLLAB (Robot Learning Laboratory), we study theory and robot learning applications including robotics, computer vision and machine learning.
Research Interests & Projects
Robotics: Deep reinforcement learning, Robust learning from demonstration, Optimal control
Computer vision: Situation understanding, 3D shape & action reconstruction
Machine learning: Deep learning, Nested sparse newtork, Transfer learning
Computer vision: Situation understanding, 3D shape & action reconstruction
Machine learning: Deep learning, Nested sparse newtork, Transfer learning
Journals & Patents
[1] Donghoon Lee, Ming-Hsuan Yang, and Songhwai Oh, "Head and Body Orientation Estimation Using Convolutional Random Projection Forests," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
[2] Junghun Suh, Joonsig Gong, and Songhwai Oh, "Fast Sampling-Based Cost-Aware Path Planning with Nonmyopic Extensions Using Cross Entropy," IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1313-1326, Dec. 2017.
[3] Jungchan Cho, Minsik Lee, and Songhwai Oh, "Complex Non-Rigid 3D Shape Recovery Using a Procrustean Normal Distribution Mixture Model," International Journal of Computer Vision, vol. 117, no. 3, pp. 226-246, May 2016.
[4] Eunwoo Kim, Chanho Ahn, and Songhwai Oh, "NestedNet: Learning Nested Sparse Structures in Deep Neural Networks," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018.
[5] Minsik Lee, Jungchan Cho, and Songhwai Oh, "Consensus of Non-Rigid Reconstructions," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016. (Oral Presentation, Acceptance Rate: 3.9%)
[6] 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)
[2] Junghun Suh, Joonsig Gong, and Songhwai Oh, "Fast Sampling-Based Cost-Aware Path Planning with Nonmyopic Extensions Using Cross Entropy," IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1313-1326, Dec. 2017.
[3] Jungchan Cho, Minsik Lee, and Songhwai Oh, "Complex Non-Rigid 3D Shape Recovery Using a Procrustean Normal Distribution Mixture Model," International Journal of Computer Vision, vol. 117, no. 3, pp. 226-246, May 2016.
[4] Eunwoo Kim, Chanho Ahn, and Songhwai Oh, "NestedNet: Learning Nested Sparse Structures in Deep Neural Networks," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2018.
[5] Minsik Lee, Jungchan Cho, and Songhwai Oh, "Consensus of Non-Rigid Reconstructions," in Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016. (Oral Presentation, Acceptance Rate: 3.9%)
[6] 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)
