引用本概念的论文(6)
- IDEA:通过效果对齐实现对动力学失配不敏感的仿真到现实迁移多智能体控制方法 IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control arXiv 2606.26575
- CORE 规划器:未知环境下面向上下文记忆的强化学习机器人导航 CORE Planner: Contextual-memory Oriented Reinforcement-learning in Unknown Environments for Robot Navigation arXiv 2606.29222
- 基于强化学习的轮滑人形机器人控制 Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot arXiv 2606.31807
- 面向轨迹跟踪的预期性强化学习 Anticipatory Reinforcement Learning for Trajectory Tracking arXiv 2607.03132
- 衡量仿真到现实的差距:为人工智能物联网系统中的强化学习设计经济实惠的真实世界基准平台 Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems arXiv 2607.10309
- ExToken:利用离散行为先验实现视觉-语言-动作强化学习中的高效探索 ExToken: Leveraging Discrete Behavioral Priors for Efficient Exploration in Vision-Language-Action Reinforcement Learning arXiv 2607.12931