| 1 |
Behavior Foundation Model for Humanoid Robots |
提出人形机器人行为基础模型,提升通用性和泛化能力 |
humanoid humanoid robot humanoid control |
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| 2 |
LeVR: A Modular VR Teleoperation Framework for Imitation Learning in Dexterous Manipulation |
LeVR:用于灵巧操作模仿学习的模块化VR遥操作框架 |
manipulation dexterous hand dexterous manipulation |
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| 3 |
Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control |
提出解耦反应力控制与步态控制的动态自适应腿足运动策略,提升泛化性 |
legged locomotion locomotion locomotion policy |
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| 4 |
CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks |
CRAFT:利用具身智能自主指导多机器人强化学习,解决复杂协作任务 |
quadruped manipulation bi-manual |
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| 5 |
SeqVLA: Sequential Task Execution for Long-Horizon Manipulation with Completion-Aware Vision-Language-Action Model |
SeqVLA:用于长时程操作的具有完成感知能力的视觉-语言-动作模型,解决序列任务执行问题。 |
manipulation vision-language-action VLA |
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| 6 |
DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion |
DreamControl:通过引导扩散实现受人类启发的全身人形机器人场景交互控制 |
humanoid humanoid control sim-to-real |
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| 7 |
Whole-body Motion Control of an Omnidirectional Wheel-Legged Mobile Manipulator via Contact-Aware Dynamic Optimization |
提出一种接触感知的全身动态优化方法,用于全向轮腿式移动机械臂的运动控制。 |
quadruped legged robot locomotion |
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| 8 |
Dual-Actor Fine-Tuning of VLA Models: A Talk-and-Tweak Human-in-the-Loop Approach |
提出人机协作的双演员微调框架以提升VLA模型的任务表现 |
manipulation reinforcement learning policy learning |
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| 9 |
CLAW: A Vision-Language-Action Framework for Weight-Aware Robotic Grasping |
CLAW:一种用于重量感知机器人抓取的视觉-语言-动作框架 |
manipulation dual-arm vision-language-action |
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| 10 |
Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models |
提出并行深度状态空间模型与Transformer的预操作对齐预测方法,提升机器人操作成功率。 |
manipulation open-vocabulary open vocabulary |
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| 11 |
FSR-VLN: Fast and Slow Reasoning for Vision-Language Navigation with Hierarchical Multi-modal Scene Graph |
提出FSR-VLN,结合分层多模态场景图与快慢推理,提升视觉语言导航性能。 |
humanoid humanoid robot Unitree |
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| 12 |
PhysicalAgent: Towards General Cognitive Robotics with Foundation World Models |
PhysicalAgent:基于世界模型的通用认知机器人框架,实现迭代推理和闭环执行。 |
humanoid manipulation bi-manual |
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| 13 |
Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move |
提出一种分散式策略,实现多足机器人协同搬运无抓取物体 |
quadruped locomotion sim2real |
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| 14 |
Language Conditioning Improves Accuracy of Aircraft Goal Prediction in Untowered Airspace |
提出语言条件约束的飞机目标预测框架,提升非塔台空域自主飞行安全性 |
motion planning large language model multimodal |
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| 15 |
Reinforcement Learning for Robotic Insertion of Flexible Cables in Industrial Settings |
提出基于强化学习和基础模型的柔性电缆机器人插入方法,实现零样本部署。 |
sim-to-real reinforcement learning foundation model |
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| 16 |
FlightDiffusion: Revolutionising Autonomous Drone Training with Diffusion Models Generating FPV Video |
FlightDiffusion:利用扩散模型生成FPV视频,革新无人机自主训练 |
sim-to-real policy learning physically plausible |
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| 17 |
SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks |
SHaRe-RL:面向高柔性工业装配的结构化交互式强化学习 |
manipulation reinforcement learning |
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| 18 |
Flexible and Foldable: Workspace Analysis and Object Manipulation Using a Soft, Interconnected, Origami-Inspired Actuator Array |
提出一种基于软性互连折纸结构的柔性分布式操作器,用于提升物体操作能力。 |
manipulation |
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| 19 |
Repulsive Trajectory Modification and Conflict Resolution for Efficient Multi-Manipulator Motion Planning |
提出基于斥力轨迹修正的多机械臂运动规划方法,提升效率并解决冲突 |
motion planning |
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| 20 |
Track Any Motions under Any Disturbances |
Any2Track:强化学习框架,实现人形机器人复杂运动和抗扰动追踪 |
humanoid sim2real Unitree |
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| 21 |
RLBind: Adversarial-Invariant Cross-Modal Alignment for Unified Robust Embeddings |
RLBind:对抗不变跨模态对齐,用于统一鲁棒嵌入 |
manipulation zero-shot transfer |
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| 22 |
HGACNet: Hierarchical Graph Attention Network for Cross-Modal Point Cloud Completion |
HGACNet:用于跨模态点云补全的分层图注意力网络 |
manipulation spatial relationship |
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| 23 |
CDFlow: Generative Gradient Flows for Configuration Space Distance Fields via Neural ODEs |
CDFlow:利用神经ODE生成配置空间距离场的梯度流,提升高自由度机器人运动规划性能。 |
motion planning |
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