cs.RO（2025-10-18）
📊 共 6 篇论文 | 🔗 2 篇有代码
🎯 兴趣领域导航
支柱九：具身大模型 (Embodied Foundation Models) (3 🔗1)
支柱一：机器人控制 (Robot Control) (2)
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) (3 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | Do What You Say: Steering Vision-Language-Action Models via Runtime Reasoning-Action Alignment Verification | 提出基于运行时推理-行动对齐验证的策略引导方法，提升VLA模型在机器人任务中的泛化性。 | vision-language-action VLA instruction following | ✅ | |
| 2 | Semi-Peaucellier Linkage and Differential Mechanism for Linear Pinching and Self-Adaptive Grasping | 提出SP-Diff平行夹爪系统，通过半反演连杆和差动机构实现线性夹取和自适应抓取。 | multimodal | ||
| 3 | What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics | 构建面向可解释机器人的用户问题数据集，助力提升人机交互能力 | large language model |
🔬 支柱一：机器人控制 (Robot Control) (2 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 4 | MoS-VLA: A Vision-Language-Action Model with One-Shot Skill Adaptation | MoS-VLA：基于技能组合的视觉-语言-动作模型，实现机器人单样本技能迁移 | manipulation vision-language-action VLA | ||
| 5 | SPOT: Sensing-augmented Trajectory Planning via Obstacle Threat Modeling | SPOT：基于障碍物威胁建模的感知增强无人机轨迹规划 | motion planning |
🔬 支柱三：空间感知与语义 (Perception & Semantics) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 6 | DIV-Nav: Open-Vocabulary Spatial Relationships for Multi-Object Navigation | DIV-Nav：利用开放词汇空间关系进行多目标导航 | semantic mapping semantic map open-vocabulary | ✅ |