cs.RO(2024-05-23)

📊 共 14 篇论文 | 🔗 1 篇有代码

🎯 兴趣领域导航

支柱一:机器人控制 (Robot Control) (5) 支柱三:空间感知与语义 (Perception & Semantics) (4) 支柱九:具身大模型 (Embodied Foundation Models) (3 🔗1) 支柱二:RL算法与架构 (RL & Architecture) (2)

🔬 支柱一:机器人控制 (Robot Control) (5 篇)

#题目一句话要点标签🔗
1 A Unification Between Deep-Learning Vision, Compartmental Dynamical Thermodynamics, and Robotic Manipulation for a Circular Economy 融合深度学习视觉、热力学和机器人操作,为循环经济构建统一框架 manipulation reinforcement learning
2 Visuo-Tactile Keypoint Correspondences for Object Manipulation 提出基于视觉-触觉关键点对应关系的物体操作方法,实现精准操作。 manipulation
3 Evolution and learning in differentiable robots 提出基于可微仿真的进化学习框架,实现机器人形态与控制策略的协同优化。 sim2real differentiable simulation
4 ReachBot Field Tests in a Mojave Desert Lava Tube as a Martian Analog ReachBot:一种用于火星模拟环境熔岩管探测的可伸缩臂机器人 locomotion
5 Optimal Whole Body Trajectory Planning for Mobile Manipulators in Planetary Exploration and Construction 提出Optimal Whole Body Planner以解决移动操控器轨迹规划问题 motion planning

🔬 支柱三:空间感知与语义 (Perception & Semantics) (4 篇)

#题目一句话要点标签🔗
6 Advancements in Translation Accuracy for Stereo Visual-Inertial Initialization 提出基于3自由度BA的视觉惯性初始化方法,提升平移精度。 visual odometry visual SLAM
7 CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments CoPeD:提出真实场景下多机器人协同感知数据集,促进高层场景理解研究 scene understanding
8 Efficient Robot Learning for Perception and Mapping 研究高效机器人学习方法,降低感知与建图对人工标注的依赖 scene understanding
9 VINS-Multi: A Robust Asynchronous Multi-camera-IMU State Estimator 提出VINS-Multi,解决异步多相机-IMU系统的鲁棒状态估计问题。 VIO

🔬 支柱九:具身大模型 (Embodied Foundation Models) (3 篇)

#题目一句话要点标签🔗
10 A Survey on Vision-Language-Action Models for Embodied AI 对具身智能中视觉-语言-动作模型(VLA)的全面综述 embodied AI vision-language-action VLA
11 Learning Multimodal Confidence for Intention Recognition in Human-Robot Interaction 提出BMCLOP框架,提升人机交互中多模态意图识别的置信度和准确率 multimodal
12 Skip-SCAR: Hardware-Friendly High-Quality Embodied Visual Navigation Skip-SCAR:面向硬件友好的高质量具身视觉导航框架 embodied AI

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

#题目一句话要点标签🔗
13 Agentic Skill Discovery 提出Agentic Skill Discovery框架,利用LLM自主发现机器人技能 reinforcement learning large language model language conditioned
14 Transformers for Image-Goal Navigation 提出基于Transformer的图像目标导航模型,解决长时程导航问题 reinforcement learning scene understanding

⬅️ 返回 cs.RO 首页 · 🏠 返回主页