Zhenyu Wu
4 indexed papers
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UniIntervene proposes an agentic intervention model for human-in-the-loop reinforcement learning to reduce human interventions and improve policy efficiency.
The paper proposes a real-to-real demonstration augmentation framework, R2RDreamer, to improve spatial generalization in imitation-learned manipulation policies.
The paper presents MacAgentBench, a comprehensive macOS agent benchmark for evaluating computer use agents.
This paper explores the use of Large Language Models (LLMs) in data fusion tasks for tabular data and shows their superiority over traditional methods.