C
Chao Yang
Researcher at Tsinghua University
Publications - 26
Citations - 2577
Chao Yang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Reinforcement learning & GRASP. The author has an hindex of 9, co-authored 26 publications receiving 1205 citations.
Papers
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Book ChapterDOI
A Survey on Deep Transfer Learning
TL;DR: Deep transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates researchers to use transfer learning to solve the problem of insufficient training data as mentioned in this paper.
Posted Content
A Survey on Deep Transfer Learning
TL;DR: This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications and defined deep transfer learning, category and review the recent research works based on the techniques used inDeep transfer learning.
Proceedings Article
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
TL;DR: It is proved that the gap between LfD and LfO actually lies in the disagreement of inverse dynamics models between the imitator and expert, if following the modeling approach of GAIL and the upper bound of this gap is revealed by a negative causal entropy which can be minimized in a model-free way.
Journal ArticleDOI
Robotic grasping using visual and tactile sensing
TL;DR: A grasp detection deep network is first proposed to detect the grasp rectangle from the visual image, then a new metric using tactile sensing is designed to assess the stability of the grasp, which demonstrates that the grasp success rate can be improved significantly in real world scenarios.
Journal ArticleDOI
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
TL;DR: In this paper, an imperfect expert setting for RLfD is defined, and the expert guidance is used as a soft constraint on regulating the policy exploration of the agent, which eventually leads to a constrained optimization problem.