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Xinghao Du

2 accepted papers

2026

Hierarchical Value-Decomposed Offline Reinforcement Learning for Whole-Body Control

ICLR 2026poster

Scaling imitation learning to high-DoF whole-body robots is fundamentally limited by the \textbf{curse of dimensionality} and the prohibitive cost of collecting expert demonstrations. We argue that the core bottleneck is paradigmatic: real-world supervision for whole-body control is inherently imper…

Cited by 0SourceScholar
2025

Improving Reward Model Generalization from Adversarial Process Enhanced Preferences

ICML 2025poster

In sequential decision-making, the reward function serves as the primary supervision signal, guiding agents to acquire the desired behaviors. Traditional reward modeling methods rely heavily on human expertise, limiting their scalability. Automated preference generation from suboptimal demonstration…

Cited by 0SourcePDFScholar