cs.RO（2024-08-15）

📊 共 10 篇论文 | 🔗 2 篇有代码

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支柱一：机器人控制 (Robot Control) (7 🔗2) 支柱二：RL算法与架构 (RL & Architecture) (1) 支柱三：空间感知与语义 (Perception & Semantics) (1) 支柱九：具身大模型 (Embodied Foundation Models) (1)

🔬 支柱一：机器人控制 (Robot Control) (7 篇)

#题目一句话要点标签🔗⭐
1 Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model 提出基于具身语言模型的类人机器人自主行为规划框架，用于复杂环境下的移动操作任务 humanoid humanoid robot manipulation
2 Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models Polaris：基于Syn2Real视觉定位与LLM的开放式交互机器人操作 manipulation large language model visual grounding ✅
3 General-purpose Clothes Manipulation with Semantic Keypoints CLASP：基于语义关键点的通用衣物操作方法 manipulation dual-arm
4 HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning HyperTaxel：通过对比学习实现Taxel触觉信号的超分辨率重建 sim-to-real contrastive learning
5 A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts 提出一种基于KAN的无冲突、低速降强化学习决策系统，用于环岛交互式驾驶 model predictive control reinforcement learning
6 Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach 提出基于Move Blocking的Tube-MPC，实现长时域鲁棒机动规划 MPC model predictive control
7 Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks 提出一种模式可切换的光学触觉传感方法，适用于机器人操作和感知任务。 manipulation ✅

🔬 支柱二：RL算法与架构 (RL & Architecture) (1 篇)

#题目一句话要点标签🔗⭐
8 Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control 提出物理引导的深度强化学习主动悬架控制系统，提升车辆行驶舒适性和稳定性。 reinforcement learning deep reinforcement learning DRL

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

#题目一句话要点标签🔗⭐
9 VLPG-Nav: Object Navigation Using Visual Language Pose Graph and Object Localization Probability Maps VLPG-Nav：利用视觉语言位姿图和物体定位概率图实现物体导航 open-vocabulary open vocabulary

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

#题目一句话要点标签🔗⭐
10 Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation 提出Nl2Hltl2Plan框架，利用分层时序逻辑实现自然语言到多机器人任务规划的扩展。 large language model

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