Adaptive gait control for humanoid robots operating in unstructured real-world environments remains a critical open problem. Existing approaches, including classical model-predictive controllers and reinforcement-learning-based policies, exhibit limited cross-terrain generalisation and require costly retraining when en...
These results show that verified synthetic environments can serve as a scalable and reliable training substrate for compact web agents, shifting synthetic webagent learning from surface-level plausibility toward executable, state-grounded supervision.
Cheng-Hao Zhang, Yu-Xiang Cheng, Sai-Sai Hu et al.· 0 citations
Results show that organizing solver feedback into generation, verification, and repair roles enables more scalable and faithful annotation-free symbolic planning.
Cheng-Hao Zhang, Yi-Kai Mao, Hao-Yu Gao et al.· 0 citations
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