cs.RO（2024-05-21）

📊 共 11 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 🔗1) 支柱九：具身大模型 (Embodied Foundation Models) (4 🔗1) 支柱三：空间感知与语义 (Perception & Semantics) (2 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (1)

🔬 支柱一：机器人控制 (Robot Control) (4 篇)

#	题目	一句话要点	标签	🔗	⭐
1	Towards Using Fast Embedded Model Predictive Control for Human-Aware Predictive Robot Navigation	提出HuMAN-MPC，利用快速嵌入式MPC实现人机共融环境下的预测性机器人导航	MPC model predictive control human motion
2	Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers	提出基于对抗攻击的评估方法，揭示学习型四足机器人控制器的脆弱性	quadruped legged locomotion locomotion	✅
3	One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation	提出基于不变性匹配的单样本模仿学习算法IMOP，用于机器人操作任务。	manipulation sim-to-real policy learning
4	BeadSight: An Inexpensive Tactile Sensor Using Hydro-Gel Beads	BeadSight：一种基于水凝胶珠的低成本可更换触觉传感器，用于机器人操作。	manipulation

🔬 支柱九：具身大模型 (Embodied Foundation Models) (4 篇)

#	题目	一句话要点	标签	🔗	⭐
5	A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddings	综述性研究：机器人语言理解中符号表示与嵌入表示的权衡	large language model
6	Pure Planning to Pure Policies and In Between with a Recursive Tree Planner	提出递归树规划器，融合规划与策略学习，提升任务泛化能力。	zero-shot transfer
7	SmartFlow: Robotic Process Automation using LLMs	SmartFlow：利用大型语言模型实现更智能的机器人流程自动化	large language model
8	Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension	提出Talk2Radar数据集和T-RadarNet模型，用于毫米波雷达场景下的3D指代表达式理解	visual grounding	✅

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

#	题目	一句话要点	标签	🔗	⭐
9	NV-LIO: LiDAR-Inertial Odometry using Normal Vectors Towards Robust SLAM in Multifloor Environments	NV-LIO：利用法向量增强LiDAR惯性里程计在多层室内环境下的鲁棒SLAM	LIO	✅
10	A Novel Methodology for Autonomous Planetary Exploration Using Multi-Robot Teams	提出一种多机器人协同的行星自主探索方法，提升探索效率和安全性	traversability

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

#	题目	一句话要点	标签	🔗	⭐
11	Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones	提出基于深度强化学习的无人机搜救方法，优化野外环境下的搜寻路径。	reinforcement learning deep reinforcement learning

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