| 1 |
A Hierarchical Framework for Humanoid Locomotion with Supernumerary Limbs |
提出一种分层控制框架,提升超冗余肢人形机器人运动稳定性 |
humanoid humanoid robot humanoid locomotion |
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| 2 |
HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments |
提出HAFO框架以解决人形机器人在强交互环境中的运动控制问题 |
humanoid humanoid robot humanoid locomotion |
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| 3 |
ACE-F: A Cross Embodiment Foldable System with Force Feedback for Dexterous Teleoperation |
ACE-F:一种具有力反馈的跨具身可折叠遥操作系统 |
manipulation dexterous manipulation teleoperation |
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| 4 |
OVAL-Grasp: Open-Vocabulary Affordance Localization for Task Oriented Grasping |
OVAL-Grasp:面向任务的开放词汇抓取方法,提升机器人操作灵活性。 |
grasping grasp localization |
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| 5 |
Arcadia: Toward a Full-Lifecycle Framework for Embodied Lifelong Learning |
Arcadia:面向具身终身学习的全生命周期框架,提升导航与操作能力。 |
manipulation representation learning scene reconstruction |
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| 6 |
Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning |
提出S$^2$-NNDS,学习安全稳定机器人运动,解决复杂动态环境下的运动规划问题。 |
motion planning |
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| 7 |
ArtiBench and ArtiBrain: Benchmarking Generalizable Vision-Language Articulated Object Manipulation |
提出ArtiBench和ArtiBrain,用于评估和提升通用视觉语言可动对象操作能力。 |
manipulation |
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| 8 |
ShapeForce: Low-Cost Soft Robotic Wrist for Contact-Rich Manipulation |
ShapeForce:低成本软体机器人腕部,用于接触丰富的操作任务 |
manipulation |
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| 9 |
Bootstrap Dynamic-Aware 3D Visual Representation for Scalable Robot Learning |
AFRO:用于可扩展机器人学习的动态感知3D视觉表征自监督框架 |
manipulation diffusion policy representation learning |
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| 10 |
Hibikino-Musashi@Home 2025 Team Description Paper |
Hibikino-Musashi@Home 2025:面向家庭服务的机器人系统开发与个性化适应研究 |
running navigation |
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| 11 |
Unifying Perception and Action: A Hybrid-Modality Pipeline with Implicit Visual Chain-of-Thought for Robotic Action Generation |
提出VITA框架,通过隐式视觉CoT统一感知与动作,提升机器人动作生成能力。 |
manipulation motion planning |
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| 12 |
Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy |
提出DA-SIP,通过动态调整计算量,提升扩散模型和流模型在机器人控制中的效率。 |
manipulation imitation learning |
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| 13 |
Collaborate sim and real: Robot Bin Packing Learning in Real-world and Physical Engine |
提出一种混合强化学习框架,结合物理引擎模拟与真实数据反馈,解决机器人装箱稳定性问题。 |
domain randomization reinforcement learning |
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| 14 |
How Robot Kinematics Influence Human Performance in Virtual Robot-to-Human Handover Tasks |
研究机器人运动学对虚拟人机交接任务中人类表现的影响 |
manipulation |
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