cs.RO（2024-12-09）

📊 共 14 篇论文

🎯 兴趣领域导航

支柱一：机器人控制 (Robot Control) (9) 支柱二：RL算法与架构 (RL & Architecture) (3) 支柱九：具身大模型 (Embodied Foundation Models) (1) 支柱三：空间感知与语义 (Perception & Semantics) (1)

🔬 支柱一：机器人控制 (Robot Control) (9 篇)

#题目一句话要点标签🔗⭐
1 AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation AnyBimanual：迁移单手策略至通用双手操作，解决数据稀缺问题 manipulation bi-manual bimanual manipulation
2 ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks ManiSkill-HAB：用于家庭重排任务中低级操作的基准测试 manipulation reinforcement learning imitation learning
3 Non-Prehensile Tool-Object Manipulation by Integrating LLM-Based Planning and Manoeuvrability-Driven Controls 提出基于LLM规划和可操纵性驱动控制的非抓取工具-物体操作方法 manipulation affordance large language model
4 Collision-Inclusive Manipulation Planning for Occluded Object Grasping via Compliant Robot Motions 提出碰撞容错操作规划框架，通过柔顺运动实现遮挡物体的抓取 manipulation dual-arm
5 Sparse Identification of Nonlinear Dynamics-based Model Predictive Control for Multirotor Collision Avoidance 提出基于SINDy的MPC多旋翼避障方法，解决未知模型和不确定性下的轨迹跟踪问题。 MPC model predictive control
6 Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion 提出基于下肢运动学状态的地面扰动检测方法，提升外骨骼机器人控制能力 locomotion
7 Haptics in Micro- and Nano-Manipulation 针对微纳操控，论文提出基于触觉反馈的磁驱动微机器人遥操作框架。 manipulation
8 Enhancing Robotic System Robustness via Lyapunov Exponent-Based Optimization 提出基于李雅普诺夫指数优化的方法，提升机器人系统的鲁棒性 locomotion differentiable simulation
9 P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policies P3-PO：利用先验点信息提升机器人策略的视觉空间泛化能力 manipulation policy learning

🔬 支柱二：RL算法与架构 (RL & Architecture) (3 篇)

#题目一句话要点标签🔗⭐
10 Vision-Based Deep Reinforcement Learning of UAV Autonomous Navigation Using Privileged Information 提出基于特权信息的深度强化学习无人机自主导航算法，解决部分可观测环境下的高速导航问题。 reinforcement learning deep reinforcement learning privileged information
11 A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO 提出基于Recurrent PPO的可扩展去中心化强化学习框架，用于UAV目标定位 reinforcement learning PPO
12 CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction CARP：通过粗到细自回归预测学习机器人视觉运动策略 policy learning

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

#题目一句话要点标签🔗⭐
13 Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks Uni-NaVid：统一具身导航任务的视频视觉-语言-动作模型 vision-language-action VLA

🔬 支柱三：空间感知与语义 (Perception & Semantics) (1 篇)

#题目一句话要点标签🔗⭐
14 On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events 提出一种低延迟单目深度估计的设备端自监督学习方法，适用于资源受限的敏捷机器人。 depth estimation monocular depth

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