10 citations · 29 across the 11 of their papers we have counts for
3 papers · 1 filter
HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration
Xingyu Liu, Deepak Pathak, Kris M. Kitani
The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the…
Tripartite: Tackle Noisy Labels by a More Precise Partition
Xuefeng Liang, Longshan Yao, Xingyu Liu +1
Samples in large-scale datasets may be mislabeled due to various reasons, and Deep Neural Networks can easily over-fit to the noisy label data. To tackle this problem, the key poin…
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
Xingyu Liu, Deepak Pathak, Kris M. Kitani
A popular paradigm in robotic learning is to train a policy from scratch for every new robot. This is not only inefficient but also often impractical for complex robots. In this wo…