4 papers
Weight Scope Alignment: A Frustratingly Easy Method for Model Merging
Yichu Xu, Xin-Chun Li, Le Gan +1
Merging models becomes a fundamental procedure in some applications that consider model efficiency and robustness. The training randomness or Non-I.I.D. data poses a huge challenge…
SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets
Shenghua Wan, Ziyuan Chen, Le Gan +2
Model-based offline reinforcement Learning (RL) is a promising approach that leverages existing data effectively in many real-world applications, especially those involving high-di…
SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring
Kaichen Huang, Minghao Shao, Shenghua Wan +4
In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation…
DIDA: Denoised Imitation Learning based on Domain Adaptation
Kaichen Huang, Hai-Hang Sun, Shenghua Wan +4
Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on th…