机器人扩散策略训练新思路，先验映射协同进化提升性能，ICML 2026 收录值得关注
arXiv:2512.02581v3 Announce Type: replace Abstract: Reinforcement learning (RL) faces a persistent tension: policies that are stable to optimize (e.g.…
用Kolmogorov方程将扩散策略提升至Cameron-Martin空间，消除离散化带来的时间漂移，提升长期部署鲁棒性。
arXiv:2606.18186v1 Announce Type: cross Abstract: Finite-dimensional (FD) diffusion policies exhibit temporal drift owing to discretization artifacts …
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arXiv:2606.13795v1 Announce Type: new Abstract: RL post-training has become increasingly pivotal for improving diffusion policies, but existing diffus…
离线扩散策略破解多用户延迟调度难题，新视角提升决策效率。
arXiv:2501.12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, i…