cs.RO（2024-07-11）

📊 共 13 篇论文 | 🔗 1 篇有代码

🎯 兴趣领域导航

支柱一：机器人控制 (Robot Control) (10 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (2) 支柱九：具身大模型 (Embodied Foundation Models) (1)

🔬 支柱一：机器人控制 (Robot Control) (10 篇)

#题目一句话要点标签🔗⭐
1 A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation 提出基于深度强化学习的框架，通过系统辨识和领域随机化缩小ASV导航中的Sim-to-Real差距。 sim-to-real domain randomization reinforcement learning
2 Real-Time Anomaly Detection and Reactive Planning with Large Language Models 提出基于LLM嵌入空间的实时异常检测与反应式规划框架，提升机器人系统安全性。 model predictive control large language model foundation model
3 Collaborative Object Manipulation on the Water Surface by a UAV-USV Team Using Tethers 提出一种基于UAV-USV协同的系绳水面物体操控方法 manipulation MPC model predictive control
4 HACMan++: Spatially-Grounded Motion Primitives for Manipulation HACMan++：面向操作任务的空间定位运动原语，提升泛化性与鲁棒性 manipulation sim-to-real reinforcement learning
5 Model Predictive Control For Mobile Manipulators Based On Neural Dynamics(Extended version) 提出基于神经动力学的移动机械臂模型预测控制方案，实现高精度轨迹跟踪 model predictive control
6 GCS*: Forward Heuristic Search on Implicit Graphs of Convex Sets 提出GCS*算法以解决凸集图上的最短路径问题 motion planning
7 Hardware Neural Control of CartPole and F1TENTH Race Car 提出基于FPGA硬件神经网络控制，提升CartPole和F1TENTH赛车控制性能 model predictive control
8 An Economic Framework for 6-DoF Grasp Detection 提出EconomicGrasp框架，以经济的监督方式实现高效的6自由度抓取检测。 manipulation ✅
9 PINN-Ray: A Physics-Informed Neural Network to Model Soft Robotic Fin Ray Fingers 提出PINN-Ray，利用物理信息神经网络建模软体机器人Fin Ray手指的复杂形变。 sim-to-real
10 Embodying Control in Soft Multistable Robots from Morphofunctional Co-design 提出基于形态功能协同设计的软体多稳态机器人控制方法 locomotion

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

#题目一句话要点标签🔗⭐
11 PrefCLM: Enhancing Preference-based Reinforcement Learning with Crowdsourced Large Language Models PrefCLM：利用众包LLM增强基于偏好的强化学习，提升人机交互体验 reinforcement learning large language model
12 Imitation Learning for Robotic Assisted Ultrasound Examination of Deep Venous Thrombosis using Kernelized Movement Primitives 提出基于核化运动原语的模仿学习方法，用于机器人辅助深静脉血栓超声检查。 imitation learning

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

#题目一句话要点标签🔗⭐
13 Robotic Control via Embodied Chain-of-Thought Reasoning 提出具身思维链（ECoT）方法，提升VLA模型在机器人控制中的泛化能力。 vision-language-action VLA chain-of-thought

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