| 1 |
From Language to Locomotion: Retargeting-free Humanoid Control via Motion Latent Guidance |
RoboGhost:提出一种无重定向的语言引导人形机器人运动控制框架 |
humanoid humanoid robot humanoid control |
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| 2 |
VLA^2: Empowering Vision-Language-Action Models with an Agentic Framework for Unseen Concept Manipulation |
VLA^2:利用Agent框架增强VLA模型处理未见概念操作的能力 |
manipulation vision-language-action VLA |
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| 3 |
Open TeleDex: A Hardware-Agnostic Teleoperation System for Imitation Learning based Dexterous Manipulation |
Open TeleDex:一个硬件无关的灵巧操作模仿学习遥操作系统 |
manipulation dexterous hand dexterous manipulation |
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| 4 |
Towards Adaptable Humanoid Control via Adaptive Motion Tracking |
AdaMimic:基于自适应运动跟踪的通用人形机器人控制方法 |
humanoid humanoid robot humanoid control |
✅ |
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| 5 |
Architecture Is All You Need: Diversity-Enabled Sweet Spots for Robust Humanoid Locomotion |
提出分层控制架构,提升人形机器人复杂地形的鲁棒运动性能 |
humanoid humanoid locomotion locomotion |
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| 6 |
Generative Models From and For Sampling-Based MPC: A Bootstrapped Approach For Adaptive Contact-Rich Manipulation |
提出生成预测控制框架以提升接触丰富操作的采样效率 |
quadruped manipulation loco-manipulation |
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| 7 |
Risk-Aware Reinforcement Learning with Bandit-Based Adaptation for Quadrupedal Locomotion |
提出基于Bandit自适应的风险感知强化学习,提升四足机器人运动鲁棒性 |
quadruped locomotion Unitree |
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| 8 |
Spatially anchored Tactile Awareness for Robust Dexterous Manipulation |
提出SaTA框架,通过空间锚定的触觉感知实现鲁棒的灵巧操作 |
manipulation dexterous manipulation bi-manual |
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| 9 |
RM-RL: Role-Model Reinforcement Learning for Precise Robot Manipulation |
RM-RL:面向精准机器人操作的角色模型强化学习 |
manipulation reinforcement learning policy learning |
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| 10 |
SkyDreamer: Interpretable End-to-End Vision-Based Drone Racing with Model-Based Reinforcement Learning |
SkyDreamer:基于模型强化学习的可解释端到端视觉无人机竞速 |
sim-to-real reinforcement learning world model |
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| 11 |
Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning |
提出AdaMoE,一种动作专用混合专家模型,提升VLA模型在机器人操作任务中的性能和效率。 |
manipulation vision-language-action VLA |
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| 12 |
RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning |
RL-100:基于真实世界强化学习的高性能机器人操作框架 |
manipulation reinforcement learning PPO |
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| 13 |
Leveraging Neural Descriptor Fields for Learning Contact-Aware Dynamic Recovery |
提出CADRE框架,利用神经描述场学习接触感知的动态恢复,提升灵巧操作的鲁棒性。 |
manipulation dexterous manipulation reinforcement learning |
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| 14 |
VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning |
VT-Refine:通过模拟微调学习基于视觉-触觉反馈的双臂装配 |
bi-manual sim-to-real reinforcement learning |
✅ |
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| 15 |
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions |
提出CBF-RL框架,通过控制屏障函数在训练中安全过滤强化学习策略 |
humanoid humanoid robot Unitree |
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| 16 |
Restoring Noisy Demonstration for Imitation Learning With Diffusion Models |
提出基于扩散模型的模仿学习框架,用于恢复含噪声的专家演示数据。 |
locomotion manipulation dexterous manipulation |
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| 17 |
Proprioceptive Image: An Image Representation of Proprioceptive Data from Quadruped Robots for Contact Estimation Learning |
提出一种基于本体感受图像的四足机器人接触估计学习方法 |
quadruped locomotion |
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| 18 |
Learning Human-Humanoid Coordination for Collaborative Object Carrying |
提出COLA算法,实现基于本体感觉的人形机器人协同搬运,提升人机协作效率。 |
humanoid reinforcement learning |
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| 19 |
Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Models |
提出CAMPD,利用上下文条件扩散模型加速多模态运动规划,提升泛化性。 |
motion planning |
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| 20 |
Prescribed Performance Control of Deformable Object Manipulation in Spatial Latent Space |
提出一种基于空间潜在空间的柔性物体操作预定性能控制方法 |
manipulation |
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| 21 |
RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks |
提出RDD:一种基于检索的分解器,用于长时任务中规划器对齐 |
manipulation vision-language-action |
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| 22 |
When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks |
引入SMART智能体,提升nuPlan自动驾驶规划器评估的真实性和可靠性 |
sim-to-real |
✅ |
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