| 1 |
Multi-Rate Nonlinear Model Predictive Control for Wall-Supported Bipedal Locomotion of Quadrupedal Robots |
提出多速率非线性模型预测控制以解决四足机器人壁支撑的双足行走问题 |
quadruped bipedal biped |
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| 2 |
LIME: Learning Intent-aware Camera Motion from Egocentric Video |
提出LIME以解决语言条件下相机运动生成问题 |
manipulation flow matching motion generation |
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| 3 |
HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum |
提出HEFT框架以解决全尺寸人形机器人重载遥操作问题 |
humanoid motion tracking locomotion |
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| 4 |
Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning |
提出执行器现实塑形以解决零-shot仿真到现实的机器人学习问题 |
legged robot humanoid humanoid robot |
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| 5 |
The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection |
提出混合动态数据收集以增强VLA空间泛化能力 |
manipulation dual-arm spatial relationship |
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| 6 |
Guided Action Flow: Q-Guided Inference for Flow-Matching Vision-Language-Action Policies |
提出Q引导推理以优化流匹配视觉-语言-动作策略 |
manipulation flow matching vision-language-action |
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| 7 |
PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation |
提出PhysMani以解决动态物体操控中的物理建模问题 |
manipulation world model world models |
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| 8 |
VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon |
提出VLA-Corrector以解决VLA政策的闭环反应不足问题 |
manipulation vision-language-action VLA |
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| 9 |
VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation |
提出VT-WAM以解决接触丰富操控中的动态建模问题 |
manipulation flow matching world action model |
✅ |
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| 10 |
WorldSample: Closed-loop Real-robot RL with World Modelling |
提出WorldSample以解决真实机器人RL中的高交互成本问题 |
manipulation reinforcement learning imitation learning |
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| 11 |
ACID: Action Consistency via Inverse Dynamics for Planning with World Models |
提出ACID框架以解决决策时间规划中的动作一致性问题 |
manipulation world model world models |
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| 12 |
CoRe: Combined Rewards with Vision-Language Model Feedback for Preference-Aligned Reinforcement Learning |
提出CoRe框架以解决强化学习中的奖励设计问题 |
manipulation reinforcement learning policy learning |
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| 13 |
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning |
提出AutoSERL框架以解决机器人强化学习中的干预问题 |
manipulation reinforcement learning imitation learning |
✅ |
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| 14 |
Imagining the Sense of Touch: Touch-Informed Manipulation via Imagined Tactile Representations |
提出TacImag框架以解决无触觉传感器的机器人操控问题 |
manipulation contact-aware |
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| 15 |
Bridge-WA: Predicting Where and How the World Changes for Robotic Action |
提出Bridge-WA以解决机器人行动中的场景变化预测问题 |
manipulation world model world models |
✅ |
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| 16 |
Choreographing the Way of Water: A Computational Framework for Aquatic Robotic Art |
提出水上机器人编排框架以应对流体动力学挑战 |
model predictive control |
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