| 1 |
Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion |
提出基于代价匹配模型预测控制的人形机器人高效强化学习方法 |
humanoid humanoid locomotion locomotion |
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| 2 |
Active Stereo-Camera Outperforms Multi-Sensor Setup in ACT Imitation Learning for Humanoid Manipulation |
主动双目相机在人形机器人操作的ACT模仿学习中优于多传感器配置 |
humanoid humanoid robot manipulation |
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| 3 |
ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation |
ManipArena:面向推理的通用机器人操作真实世界综合评估框架 |
manipulation mobile manipulation world model |
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| 4 |
FocusVLA: Focused Visual Utilization for Vision-Language-Action Models |
FocusVLA:聚焦视觉利用的视觉-语言-动作模型,提升机器人操作性能 |
manipulation dexterous manipulation vision-language-action |
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| 5 |
Pandora: Articulated 3D Scene Graphs from Egocentric Vision |
Pandora:利用第一人称视觉构建可动3D场景图,提升机器人操作能力 |
manipulation mobile manipulation egocentric |
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| 6 |
DRIVE-Nav: Directional Reasoning, Inspection, and Verification for Efficient Open-Vocabulary Navigation |
提出DRIVE-Nav以解决开放词汇目标导航中的路径效率问题 |
humanoid humanoid robot open-vocabulary |
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| 7 |
Feel Robot Feels: Tactile Feedback Array Glove for Dexterous Manipulation |
提出TAG触觉反馈手套系统,提升灵巧操作遥操作的性能与数据质量 |
manipulation dexterous manipulation teleoperation |
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| 8 |
Tac2Real: Reliable and GPU Visuotactile Simulation for Online Reinforcement Learning and Zero-Shot Real-World Deployment |
Tac2Real:用于在线强化学习和零样本迁移的可靠GPU视觉触觉模拟 |
manipulation sim-to-real reinforcement learning |
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| 9 |
Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching |
Tele-Catch:面向灵巧动态3D物体抓取的自适应遥操作框架 |
manipulation dexterous hand teleoperation |
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| 10 |
SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning |
SOLE-R1:利用视频-语言推理作为机器人强化学习的唯一奖励信号 |
manipulation reinforcement learning spatiotemporal |
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| 11 |
A Foldable and Agile Soft Electromagnetic Robot for Multimodal Navigation in Confined and Unstructured Environments |
提出一种可折叠的软电磁机器人,用于狭窄非结构化环境下的多模态导航 |
locomotion multimodal |
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| 12 |
Dynamic Lookahead Distance via Reinforcement Learning-Based Pure Pursuit for Autonomous Racing |
提出基于强化学习的Pure Pursuit算法,动态调整前瞻距离以优化自动驾驶赛车性能 |
sim-to-real reinforcement learning PPO |
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| 13 |
Serialized Red-Green-Gray: Quicker Heuristic Validation of Edges in Dynamic Roadmap Graphs |
提出基于GPU加速的SerRGG框架,加速动态路标图中的边验证,提升机器人运动规划效率。 |
motion planning |
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