cs.RO（2025-10-25）

📊 共 9 篇论文 | 🔗 3 篇有代码

🎯 兴趣领域导航

支柱一：机器人控制 (Robot Control) (4 🔗2) 支柱二：RL算法与架构 (RL & Architecture) (2) 支柱八：物理动画 (Physics-based Animation) (1) 支柱三：空间感知与语义 (Perception & Semantics) (1 🔗1) 支柱九：具身大模型 (Embodied Foundation Models) (1)

🔬 支柱一：机器人控制 (Robot Control) (4 篇)

#	题目	一句话要点	标签	🔗	⭐
1	ACG: Action Coherence Guidance for Flow-based VLA models	提出动作连贯性引导（ACG）方法，提升基于流的VLA模型在机器人操作任务中的性能	manipulation imitation learning flow matching	✅
2	EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control	EasyUUV：基于LLM的通用轻量级UUV姿态控制Sim-to-Real强化学习框架	sim-to-real reinforcement learning large language model	✅
3	Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery	提出RoboCraft框架，联合优化人形机器人控制与形态，提升跌倒恢复能力。	humanoid humanoid robot locomotion
4	RaycastGrasp: Eye-Gaze Interaction with Wearable Devices for Robotic Manipulation	RaycastGrasp：基于眼动追踪与可穿戴设备的机器人操作交互	manipulation egocentric

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

#	题目	一句话要点	标签	🔗	⭐
5	BLIP-FusePPO: A Vision-Language Deep Reinforcement Learning Framework for Lane Keeping in Autonomous Vehicles	提出BLIP-FusePPO框架以解决自动驾驶车辆的车道保持问题	reinforcement learning deep reinforcement learning policy learning
6	A Novel Multi-Timescale Stability-Preserving Hierarchical Reinforcement Learning Controller Framework for Adaptive Control in High-Dimensional Dynamical Systems	提出多时间尺度稳定性保持的层次强化学习控制框架以解决高维动态系统控制问题	reinforcement learning

🔬 支柱八：物理动画 (Physics-based Animation) (1 篇)

#	题目	一句话要点	标签	🔗	⭐
7	Estimating Continuum Robot Shape under External Loading using Spatiotemporal Neural Networks	提出时空神经网络，融合多模态数据，精确估计受载连续体机器人的形状	spatiotemporal

🔬 支柱三：空间感知与语义 (Perception & Semantics) (1 篇)

#	题目	一句话要点	标签	🔗	⭐
8	Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis	提出基于时空法线分析的动态感知LIO框架，解决动态环境下定位难题	LIO	✅

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

#	题目	一句话要点	标签	🔗	⭐
9	Bridging Perception and Reasoning: Dual-Pipeline Neuro-Symbolic Landing for UAVs in Cluttered Environments	NeuroSymLand：结合神经符号推理，提升无人机在复杂环境下的自主着陆能力	large language model foundation model

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