1 citations · 1 across the 5 of their papers we have counts for
6 papers
Harnessing VLM in Embodied RL: From Reward Regressor to Success Detector via Exploration-Augmented Key-Observation Gating
Xin Liu, Yutong Jiang, Jian Su +4
VLMs provide promising solutions to embodied reward design. Most current methods that train VLMs as reward regressors not only require extensive annotations but are also primarily…
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
Xin Liu, Yixuan Li, Yuhui Chen +3
Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human jud…
Videos are Sample-Efficient Supervisions: Behavior Cloning from Videos via Latent Representations
Xin Liu, Haoran Li, Dongbin Zhao
Humans can efficiently extract knowledge and learn skills from the videos within only a few trials and errors. However, it poses a big challenge to replicate this learning process…
Sample-efficient Unsupervised Policy Cloning from Ensemble Self-supervised Labeled Videos
Xin Liu, Yaran Chen, Haoran Li
Current advanced policy learning methodologies have demonstrated the ability to develop expert-level strategies when provided enough information. However, their requirements, inclu…
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning
Xin Liu, Yaran Chen, Dongbin Zhao
In visual Reinforcement Learning (RL), upstream representation learning largely determines the effect of downstream policy learning. Employing auxiliary tasks allows the agent to e…
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
Xin Liu, Yaran Chen, Dongbin Zhao
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Unsupervised s…