cs.RO（2025-09-05）

📊 共 11 篇论文 | 🔗 1 篇有代码

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支柱一：机器人控制 (Robot Control) (8) 支柱二：RL算法与架构 (RL & Architecture) (2) 支柱九：具身大模型 (Embodied Foundation Models) (1 🔗1)

🔬 支柱一：机器人控制 (Robot Control) (8 篇)

#题目一句话要点标签🔗⭐
1 Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots 提出基于分层降阶模型预测控制的人形机器人鲁棒步态方法 humanoid humanoid robot locomotion
2 DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation DeGuV：深度引导的视觉强化学习，提升操作任务的泛化性和可解释性 manipulation sim-to-real reinforcement learning
3 COMMET: A System for Human-Induced Conflicts in Mobile Manipulation of Everyday Tasks COMMET：用于日常任务移动操作中人机冲突处理的系统 manipulation mobile manipulation
4 RoboBallet: Planning for Multi-Robot Reaching with Graph Neural Networks and Reinforcement Learning RoboBallet：利用图神经网络和强化学习进行多机器人协同规划 motion planning reinforcement learning task and motion planning
5 Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers 针对深度学习感知误差，提出基于集合论状态估计的鲁棒模型预测控制 MPC model predictive control
6 Microrobot Vascular Parkour: Analytic Geometry-based Path Planning with Real-time Dynamic Obstacle Avoidance 提出基于解析几何的微型机器人血管导航路径规划与动态避障方法 parkour reinforcement learning
7 Ground-Aware Octree-A* Hybrid Path Planning for Memory-Efficient 3D Navigation of Ground Vehicles 提出基于Ground-Aware Octree-A*的混合路径规划算法，提升地面车辆3D导航的内存效率。 legged robot locomotion
8 Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation 提出工具感知自适应柔顺控制，用于月球土壤自主挖掘 operational space control reinforcement learning

🔬 支柱二：RL算法与架构 (RL & Architecture) (2 篇)

#题目一句话要点标签🔗⭐
9 Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics 提出基于解耦表征学习的模仿学习方法，实现机器人动作对人类指令的在线适应 imitation learning representation learning motion generation
10 Shared Autonomy through LLMs and Reinforcement Learning for Applications to Ship Hull Inspections 融合LLM与强化学习的共享自主系统，用于船体检测 reinforcement learning large language model

🔬 支柱九：具身大模型 (Embodied Foundation Models) (1 篇)

#题目一句话要点标签🔗⭐
11 FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies FLOWER：通过高效的视觉-语言-动作流策略实现通用机器人策略的大众化 vision-language-action VLA ✅

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