Transferring robotic skills from simulation to reality requires task knowledge that remains usable across differences in perception, dynamics, and embodiment. We introduce Skill2Real, an agentic policy framework that learns executable skills through a shared application programming interface (API). A Proposer-Verifier-...
Xin-Chen He, Si-Yu Ma, Chang Yu et al.· 1 citation
Model-based control can directly execute specified objectives, while learning can amortize such behaviors into reactive policies, making their combination a natural solution to multi-stage manipulation. We introduce Semantically UNified (SUN) Programs, typed executables that compile grounded relations into aligned opti...
Wei-Qi Wang, Zhi Li, Yuliang Lei et al.· 0 citations
Low-Entropy Discrete Flow (LEDFlow), a training-free sampler that adaptively orders absorption by local entropy, is proposed, and it is shown that, under entropy-error regularity, selecting the lowest-entropy positions under a fixed absorption count minimizes an upper bound on the conditional term.
Tung Sum Thomas Kwok, Yi-Dong Ouyang, Yingjia Wan et al.· 0 citations
The results establish that simulation-screened task semantics can effectively amortize control into robust policies, without demonstrations or manual dense rewards, unifying symbolic planning and data-driven execution.
Wei-Qi Wang, Zhi Li, Yuliang Lei et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
