| 1 |
BeJEPA: A Goal-Conditioned Humanoid Behavior World Model via Unsupervised RL |
提出BeJEPA以解决类人机器人控制中的目标定位问题 |
humanoid humanoid control motion tracking |
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| 2 |
CoEvolve: Self-Evolving Code Policies from Skills to Strategies for Humanoid Robots |
提出CoEvolve框架以解决多机器人协作中的协调适应问题 |
humanoid humanoid robot manipulation |
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| 3 |
Reinforcement Learning Based Multi-Terrain Adaptive Whole-Body Control for a Quadruped Manipulator |
提出基于强化学习的全身控制框架以解决四足机器人在复杂地形中的操控问题 |
quadruped legged robot whole-body control |
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| 4 |
MambaAct: Efficient Mamba Action Experts for Flow-Matching Vision-Language-Action Models |
提出MambaAct以提高流匹配视觉-语言-动作模型的效率 |
manipulation flow matching Mamba |
✅ |
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| 5 |
GRASP: Global-Local Representation Alignment and Spatial-Physical Grounding for Bimanual Robotic Manipulation |
提出GRASP框架以解决双手机器人操控中的任务成功率问题 |
manipulation bi-manual bimanual manipulation |
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| 6 |
Foundation-Model-Driven 3D Scene Graphs for Mobile Manipulation |
提出基础模型驱动的3D场景图以解决移动操作中的空间记忆问题 |
manipulation mobile manipulation foundation model |
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| 7 |
Embodiment-Aware Planning for Heterogeneous Bimanual Manipulation |
提出异构双手操作的具身感知规划以解决复杂操控问题 |
manipulation bi-manual dual-arm |
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| 8 |
Look Before You Act: Bridging Active Perception and Vision-Language-Action Models via Observation-Aligned Data Adaptation |
提出ActiveAlign以解决机器人操作中的观察状态分布敏感性问题 |
manipulation vision-language-action VLA |
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| 9 |
ORD-WM: Learning Object-Centric Rigid Dynamics for 3D World Modeling in Robotic Manipulation |
提出ORD-WM以解决机器人操作中的物体中心刚性动力学建模问题 |
manipulation model predictive control world model |
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| 10 |
No Eyes, No Problem: What Does Touch Contribute to Blind Baoding Ball Manipulation? |
提出无视力的触觉反馈机制以解决盲操控问题 |
manipulation dexterous hand dexterous manipulation |
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| 11 |
Ego2Contact: Predicting Touch from Egocentric Human Video for Contact-Rich Robot Manipulation |
提出Ego2Contact以解决机器人触觉感知不足问题 |
manipulation bi-manual reinforcement learning |
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| 12 |
Enhancing Visual Task-Redirection Attacks on Vision-Language-Action Models via Closed-Loop Optimization |
提出TRACO框架以解决视觉语言行动模型的攻击问题 |
manipulation vision-language-action VLA |
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| 13 |
VLA-Fabric: Learning Interaction for Composable Multi-Arm VLA Agents |
提出VLA-Fabric以解决多臂机器人协调问题 |
manipulation vision-language-action VLA |
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| 14 |
AWAM: Active Trajectory-Conditioned View Imagination for Robust Robot Manipulation |
提出AWAM以解决开放世界机器人操作中的感知缺口问题 |
manipulation world model world models |
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| 15 |
Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts |
提出盲目精确装配方法以解决复杂部件组装问题 |
legged robot humanoid sim-to-real |
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| 16 |
MOSS: Multimodal Origami Swamp Robot System |
提出MOSS以解决湿地监测中的多样化地形适应问题 |
locomotion multimodal |
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| 17 |
LAW: Unifying Language, Action, and World Modeling for Agentic Physical Intelligence |
提出LAW框架以统一语言、动作与世界建模,提升物理智能表现 |
manipulation world model world models |
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| 18 |
Foundation Models for Autonomous Space Robotics: Architectures, Physical Adaptation, and Validation |
提出基础模型以解决自主空间机器人任务执行问题 |
manipulation foundation model |
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| 19 |
Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm |
提出基于模仿学习的草莓 pedicel 目标采摘方法以解决精准抓取问题 |
manipulation motion planning teleoperation |
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| 20 |
Robotic Spatiotemporal Editing: A Unified Interface for Controllable Action Generation via Masked Action Reconstruction |
提出MAP接口以解决机器人行为控制与编辑问题 |
manipulation spatiotemporal |
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| 21 |
Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors |
提出AnchorQ以提高强化学习样本效率解决机器人策略改进问题 |
manipulation reinforcement learning flow matching |
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| 22 |
Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning |
提出形状感知神经时间场以解决刚体运动规划问题 |
manipulation motion planning |
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| 23 |
DefFlow: Goal-Conditioned Deformable Object Manipulation using 3D Point Flows |
提出DefFlow以解决变形物体目标导向操控问题 |
manipulation |
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| 24 |
InstAgent: A Recursively Self-Improving Agent Operating System for Heterogeneous Robot Collaboration |
提出InstAgent以解决异构机器人协作中的执行时间修正和上下文记忆问题 |
manipulation multimodal |
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| 25 |
Not All Actions Contribute Equally: Learning Precise Progress Estimation from Imprecise Demonstrations |
提出PRIME以解决机器人操作中的进度估计问题 |
manipulation reinforcement learning |
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| 26 |
Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control |
提出自适应计算预算以解决实时控制中的规划与反应问题 |
manipulation reinforcement learning |
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| 27 |
HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents |
提出HexaShell以探索VLM代理作为机器人编码代理的极限 |
manipulation |
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