cs.RO(2025-10-22)
📊 共 11 篇论文 | 🔗 2 篇有代码
🎯 兴趣领域导航
支柱一:机器人控制 (Robot Control) (5 🔗2)
支柱二:RL算法与架构 (RL & Architecture) (4)
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) (5 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | GigaBrain-0: A World Model-Powered Vision-Language-Action Model | GigaBrain-0:基于世界模型赋能的视觉-语言-动作通用机器人模型 | manipulation mobile manipulation sim2real | ||
| 2 | Using Non-Expert Data to Robustify Imitation Learning via Offline Reinforcement Learning | 利用离线强化学习,通过非专家数据增强模仿学习的鲁棒性 | manipulation reinforcement learning policy learning | ✅ | |
| 3 | Push Anything: Single- and Multi-Object Pushing From First Sight with Contact-Implicit MPC | 提出C3+算法,通过接触隐式MPC实现对多种物体的单/多目标精准推移操作。 | manipulation MPC model predictive control | ✅ | |
| 4 | Optimizing Prosthetic Wrist Movement: A Model Predictive Control Approach | 提出基于模型预测控制的义肢腕部运动优化方案,提升灵活性和用户控制。 | MPC model predictive control predictive model | ||
| 5 | TARMAC: A Taxonomy for Robot Manipulation in Chemistry | 提出TARMAC以解决化学实验室机器人操作技能缺乏的问题 | manipulation |
🔬 支柱二:RL算法与架构 (RL & Architecture) (4 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 6 | SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas | SEA:基于语义地图预测的主动探索不确定区域方法 | reinforcement learning semantic map | ||
| 7 | Imitation Learning Policy based on Multi-Step Consistent Integration Shortcut Model | 提出基于多步一致性积分捷径模型的模仿学习策略,加速机器人策略推理。 | imitation learning flow matching distillation | ||
| 8 | ProTerrain: Probabilistic Physics-Informed Rough Terrain World Modeling | ProTerrain:提出概率物理信息粗糙地形建模方法,提升机器人轨迹预测精度。 | world model traversability | ||
| 9 | Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models | 提出基于强化学习和视觉语言模型的层级DLO路径规划方法 | reinforcement learning |
🔬 支柱三:空间感知与语义 (Perception & Semantics) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 10 | Learning Affordances at Inference-Time for Vision-Language-Action Models | 提出LITEN,通过推理时学习能力提升VLA模型在复杂机器人任务中的表现 | affordance vision-language-action VLA |
🔬 支柱九:具身大模型 (Embodied Foundation Models) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 11 | LaViRA: Language-Vision-Robot Actions Translation for Zero-Shot Vision Language Navigation in Continuous Environments | LaViRA:用于连续环境零样本视觉语言导航的语言-视觉-机器人动作翻译框架 | vision-language-navigation VLN large language model |