cs.RO(2025-03-27)

📊 共 13 篇论文 | 🔗 2 篇有代码

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支柱一:机器人控制 (Robot Control) (11 🔗1) 支柱二:RL算法与架构 (RL & Architecture) (1 🔗1) 支柱三:空间感知与语义 (Perception & Semantics) (1)

🔬 支柱一:机器人控制 (Robot Control) (11 篇)

#题目一句话要点标签🔗
1 ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning ManipTrans:通过残差学习实现高效的灵巧双手动操作迁移 manipulation dexterous hand bi-manual
2 OminiAdapt: Learning Cross-Task Invariance for Robust and Environment-Aware Robotic Manipulation OminiAdapt:学习跨任务不变性,提升机器人操作的鲁棒性和环境感知能力 humanoid humanoid robot manipulation
3 Embodied Long Horizon Manipulation with Closed-loop Code Generation and Incremental Few-shot Adaptation 提出基于闭环代码生成和增量少样本自适应的具身长程操作方法 manipulation large language model multimodal
4 Cooking Task Planning using LLM and Verified by Graph Network 结合LLM与图网络的烹饪任务规划方法,提升机器人操作成功率 manipulation dual-arm motion planning
5 AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control 提出AcL框架,提升四足机器人多关节失效下的容错运动控制能力 quadruped locomotion reinforcement learning
6 Data-Driven Contact-Aware Control Method for Real-Time Deformable Tool Manipulation: A Case Study in the Environmental Swabbing 提出基于数据驱动的接触感知控制方法,用于实时柔性工具操作,以环境拭子采样为例。 manipulation contact-aware
7 Fuzzy-Logic-based model predictive control: A paradigm integrating optimal and common-sense decision making 提出基于模糊逻辑模型预测控制(FLMPC)的多机器人未知环境探索方法 MPC model predictive control
8 Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning 提出基于贝叶斯非参数先验知识的机器人长时程强化学习方法 manipulation reinforcement learning
9 Bayesian Inferential Motion Planning Using Heavy-Tailed Distributions 提出基于Student's-t分布的贝叶斯推断运动规划方法,提升低概率区域探索能力。 motion planning
10 Beyond Omakase: Designing Shared Control for Navigation Robots with Blind People 为盲人导航机器人设计共享控制模式,提升用户自主性与社交适应性 shared control
11 Haptic bilateral teleoperation system for free-hand dental procedures 针对自由手牙科手术,提出力触觉双边遥操作系统以提升精度与安全性 teleoperation

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

#题目一句话要点标签🔗
12 Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks 提出Bresa:一种受生物启发的反射性安全强化学习方法,用于接触丰富的机器人任务。 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🔗
13 STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM STAMICS:融合语义一致性和语义信息的密集RGB-D SLAM系统 open-vocabulary open vocabulary

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