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Zhenxian Liu

2 accepted papers

2026

COVR: Collaborative Optimization of VLMs and RL Agent for Visual-Based Control

AAAI 2026technical

Visual reinforcement learning (RL) suffers from poor sample efficiency due to high-dimensional observations in complex tasks. While existing works have shown that vision-language models (VLMs) can assist RL, they often focus on knowledge distillation from the VLM to RL, overlooking the potential of

Cited by 0SourcePDFScholar
2026

Perceiving the Knowledge Boundary: Uncertainty-Guided Exploration and Imagination for World Models

AAAI 2026technical

World-model-based reinforcement learning achieves high sample efficiency by learning from imagined rollouts. However, its success critically depends on the accuracy of the learned world model, which is prone to producing unrealistic or hallucinated rollouts when queried beyond its domain of competen

Cited by 0SourcePDFScholar