| 1 |
EMMA: Scaling Mobile Manipulation via Egocentric Human Data |
EMMA:利用以人为中心的视觉数据扩展移动操作模仿学习 |
manipulation mobile manipulation teleoperation |
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| 2 |
Balancing Signal and Variance: Adaptive Offline RL Post-Training for VLA Flow Models |
提出自适应强化流匹配(ARFM),提升VLA模型在复杂机器人操作任务中的动作精度。 |
manipulation reinforcement learning offline RL |
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| 3 |
FPC-VLA: A Vision-Language-Action Framework with a Supervisor for Failure Prediction and Correction |
提出FPC-VLA框架,通过监督器进行失败预测与纠正,提升机器人操作的可靠性。 |
manipulation vision-language-action VLA |
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| 4 |
Solving Robotics Tasks with Prior Demonstration via Exploration-Efficient Deep Reinforcement Learning |
提出一种探索高效的深度强化学习框架DRLR,利用先验演示解决机器人任务。 |
sim2real reinforcement learning deep reinforcement learning |
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| 5 |
Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance |
提出基于置信度感知稠密对应和触觉可供性的服装空中操作方法 |
manipulation dual-arm affordance |
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| 6 |
Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision |
Surformer v2:用于触觉与视觉表面理解的多模态分类器 |
manipulation multimodal |
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| 7 |
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation |
DEXOP:一种用于机器人灵巧操作迁移的设备 |
manipulation dexterous manipulation teleoperation |
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| 8 |
Learning Multi-Stage Pick-and-Place with a Legged Mobile Manipulator |
提出基于强化学习的多阶段移动操作方法,解决四足机器人复杂操作任务 |
quadruped manipulation mobile manipulation |
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| 9 |
Object-Reconstruction-Aware Whole-body Control of Mobile Manipulators |
提出一种面向移动机械臂的、基于目标重建感知的全身控制方法,提升三维重建效率。 |
whole-body control |
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| 10 |
Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robot |
提出基于关键点的扩散模型,加速NICOL机器人运动规划。 |
motion planning |
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| 11 |
Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement |
提出SPOT:一种基于点云变换搜索的多物体重排规划方法 |
manipulation policy learning |
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| 12 |
Lightweight Kinematic and Static Modeling of Cable-Driven Continuum Robots via Actuation-Space Energy Formulation |
提出LASEM框架,用于轻量化建模缆索驱动连续体机器人,实现实时运动规划与控制。 |
motion planning |
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