| 1 |
CheckVLA: Execution-Time Verification with Action-Conditioned World Model for Long-Horizon Mobile Manipulation |
提出CheckVLA以解决长时间移动操控中的执行验证问题 |
manipulation mobile manipulation world model |
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| 2 |
RLMM-Flow: A Flow-based Mobile Manipulation Framework with Latent-Space Reinforcement Learning |
提出RLMM-Flow以解决移动操控中的多模态动作生成问题 |
manipulation mobile manipulation motion planning |
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| 3 |
Explicit Kinematic Guidance from Analytic Concepts for Vision-Language-Action Models |
提出显式运动学引导以解决视觉-语言-动作模型的空间感知问题 |
manipulation reinforcement learning vision-language-action |
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| 4 |
ContactFlow: A video action conditioning that transfers across embodiments |
提出Contact Flow以解决视频动作条件化的跨实体转移问题 |
manipulation world model world models |
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| 5 |
Reinforcement Learning on Cost-Constrained Quadrupedal Hardware |
提出生物启发方法以解决低成本四足机器人控制中的延迟问题 |
quadruped locomotion sim-to-real |
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| 6 |
DLAM: Distributional Latent Actions with Temporal Constraints |
提出DLAM以解决机器人动作标注数据稀缺问题 |
manipulation policy learning flow matching |
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| 7 |
Speech2Grasp: Data-Efficient Transfer of Text-Conditioned Grasp Detection to Speech in Humanoid Robots |
提出Speech2Grasp以解决人形机器人语音抓取检测问题 |
humanoid humanoid robot |
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| 8 |
SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception |
提出SymmGrid以加速机器人学习过程 |
humanoid manipulation policy learning |
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| 9 |
Route by Kinematics, Act by Observation: Kinematics-Supervised Expert Routing in MoE-Augmented VLA |
提出运动学监督专家路由以解决MoE增强VLA中的专家路由问题 |
manipulation VLA |
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| 10 |
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment |
提出基于RBFN的运动规划框架以提高城市驾驶安全性 |
MPC motion planning |
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| 11 |
Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret |
提出自适应在线学习与模型预测控制以追踪未知动态 |
model predictive control |
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| 12 |
Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations |
提出HERO以解决机器人自我提升能力的挑战 |
manipulation |
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