cs.RO（2024-11-13）

📊 共 12 篇论文 | 🔗 1 篇有代码

🎯 兴趣领域导航

支柱一：机器人控制 (Robot Control) (6) 支柱二：RL算法与架构 (RL & Architecture) (3) 支柱三：空间感知与语义 (Perception & Semantics) (2) 支柱九：具身大模型 (Embodied Foundation Models) (1 🔗1)

🔬 支柱一：机器人控制 (Robot Control) (6 篇)

#题目一句话要点标签🔗⭐
1 Open-World Task and Motion Planning via Vision-Language Model Inferred Constraints 提出OWL-TAMP，利用视觉-语言模型约束实现开放世界任务与运动规划 manipulation motion planning foundation model
2 Integrative Wrapping System for a Dual-Arm Humanoid Robot 提出一种集成式双臂人形机器人包装系统，实现柔性物体的连续多步骤操作 humanoid humanoid robot manipulation
3 Offline Adaptation of Quadruped Locomotion using Diffusion Models 提出基于扩散模型的四足机器人离线步态自适应方法 quadruped locomotion ANYmal
4 Precision-Focused Reinforcement Learning Model for Robotic Object Pushing 提出基于记忆的视觉-本体强化学习模型以提高机器人推物精度 manipulation reinforcement learning
5 Experience-based Subproblem Planning for Multi-Robot Motion Planning 提出基于经验的子问题规划方法，提升多机器人运动规划效率 motion planning
6 Predictive Visuo-Tactile Interactive Perception Framework for Object Properties Inference 提出基于视觉-触觉预测交互感知的物体属性推断框架 manipulation

🔬 支柱二：RL算法与架构 (RL & Architecture) (3 篇)

#题目一句话要点标签🔗⭐
7 Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent 提出基于语法化的深度多自编码器潜在空间探索强化学习抓取框架 reinforcement learning
8 BAMAX: Backtrack Assisted Multi-Agent Exploration using Reinforcement Learning 提出BAMAX，一种基于强化学习和回溯辅助的多智能体探索方法，提升环境探索效率。 reinforcement learning
9 Goal-oriented Semantic Communication for Robot Arm Reconstruction in Digital Twin: Feature and Temporal Selections 提出面向数字孪生机器人手臂重建的语义通信框架，降低通信负载。 reinforcement learning deep reinforcement learning

🔬 支柱三：空间感知与语义 (Perception & Semantics) (2 篇)

#题目一句话要点标签🔗⭐
10 Voxeland: Probabilistic Instance-Aware Semantic Mapping with Evidence-based Uncertainty Quantification Voxeland：提出基于证据的不确定性量化概率实例感知语义地图构建方法 scene understanding semantic mapping semantic map
11 DG-SLAM: Robust Dynamic Gaussian Splatting SLAM with Hybrid Pose Optimization DG-SLAM：基于动态高斯溅射的鲁棒SLAM，采用混合位姿优化 visual SLAM gaussian splatting splatting

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

#题目一句话要点标签🔗⭐
12 DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models DART-LLM：利用LLM和依赖感知图实现多机器人任务分解与执行 large language model ✅

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