| 1 |
MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation |
提出MotionWAM以解决实时人形机器人运动控制问题 |
humanoid humanoid control locomotion |
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| 2 |
TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation |
提出TORL-VLA以解决接触丰富任务中的在线适应问题 |
manipulation reinforcement learning vision-language-action |
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| 3 |
ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models |
提出PROBEACT以解决VLA模型在操作失败时的恢复问题 |
manipulation vision-language-action VLA |
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| 4 |
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies |
提出ReCoVLA以解决视觉-语言-动作策略中的失败恢复问题 |
manipulation sim-to-real vision-language-action |
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| 5 |
MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models |
提出MemoryVLA++以解决机器人操控中的时间建模问题 |
manipulation world model world models |
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| 6 |
$ω$-EVA: Envision, Verify, and Act with Latent Interactive World Models |
提出$ω$-EVA以解决现有动作生成模型的局限性 |
bi-manual world model world models |
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| 7 |
Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models |
提出基于注意力引导的安全过滤器以解决VLA模型的碰撞问题 |
manipulation vision-language-action VLA |
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| 8 |
Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection |
提出B2FF框架以解决VLA策略的失败恢复问题 |
manipulation vision-language-action VLA |
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| 9 |
PTDL:Multi-Terrain Fall Recovery via Phase-Terrain Decoupled Learning |
提出PTDL以解决多地形下人形机器人跌倒恢复问题 |
humanoid humanoid robot locomotion |
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| 10 |
iMaC: Translating Actions into Motion and Contact Images for Embodied World Models |
提出iMaC以解决传统动作表示的局限性问题 |
manipulation world model world models |
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| 11 |
Safe Polytope-in-Polytope Motion Planning and Control with Control Barrier Functions |
提出安全多面体运动规划方法以解决狭窄环境中的机器人控制问题 |
model predictive control motion planning occupancy grid |
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| 12 |
VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands |
提出VAIC以解决人形机器人在复杂环境中的物体交互控制问题 |
humanoid humanoid robot distillation |
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| 13 |
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps |
提出SynManDex以解决机器人抓取中的人类手势转化问题 |
manipulation bi-manual affordance |
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| 14 |
Difference-Aware Retrieval Policies for Imitation Learning |
提出差异感知检索策略以解决模仿学习中的泛化问题 |
manipulation imitation learning behavior cloning |
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| 15 |
CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control |
提出CT-VAM以解决高频低级执行中的任务意图处理问题 |
manipulation vision-language-action VLA |
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| 16 |
Autonomous Obstacle Removal for Excavators through Policy Learning with Particle Simulation |
提出基于粒子模拟的自主障碍物移除策略以解决挖掘机自动化问题 |
sim-to-real policy learning curriculum learning |
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| 17 |
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience |
提出DexPIE以解决高维动作空间下的灵巧操控问题 |
manipulation dexterous hand dexterous manipulation |
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| 18 |
ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration |
提出ReGIL框架以解决单一示范下机器人学习挑战 |
manipulation imitation learning |
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| 19 |
Motion planning for hundreds of floating robots |
提出可扩展的运动规划方法以解决浮动机器人群体的碰撞避免问题 |
motion planning |
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| 20 |
Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization |
提出基于FIM的轨迹优化方法以解决无人机目标定位问题 |
trajectory optimization |
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| 21 |
Dense Force Estimation with an Event-based Optical Tactile Sensor |
提出基于事件的光学触觉传感器的密集力估计方法 |
manipulation dexterous manipulation |
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| 22 |
AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing |
提出AHA-WAM以解决世界-动作模型的时间耦合问题 |
manipulation policy learning |
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| 23 |
AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning |
提出AetheRock以解决力导向视觉触觉学习中的传感器不兼容问题 |
manipulation representation learning |
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