| 1 |
Learning Humanoid Locomotion over Challenging Terrain |
提出基于Transformer的强化学习方法,实现复杂地形下人形机器人稳健运动控制 |
humanoid humanoid robot humanoid locomotion |
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| 2 |
Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation |
提出基于可微仿真的残差策略学习方法,提升四足机器人感知控制性能 |
quadruped locomotion reinforcement learning |
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| 3 |
Latent Action Priors for Locomotion with Deep Reinforcement Learning |
提出基于隐空间动作先验的深度强化学习方法,提升机器人运动控制的自然性和鲁棒性 |
locomotion reinforcement learning deep reinforcement learning |
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| 4 |
STREAMS: An Assistive Multimodal AI Framework for Empowering Biosignal Based Robotic Controls |
STREAMS:一种辅助性多模态AI框架,用于增强基于生物信号的机器人控制 |
sim-to-real reinforcement learning deep reinforcement learning |
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| 5 |
Autoregressive Action Sequence Learning for Robotic Manipulation |
提出基于分块因果Transformer的自回归策略网络ARP,用于解决机器人操作中的通用策略设计问题。 |
manipulation Aloha |
✅ |
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| 6 |
GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs |
GenSim2:利用多模态推理LLM扩展机器人数据生成,实现零样本迁移。 |
sim-to-real language conditioned zero-shot transfer |
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| 7 |
Sampling-Based Model Predictive Control for Volumetric Ablation in Robotic Laser Surgery |
提出基于采样的模型预测控制,用于机器人激光手术中的体积消融。 |
MPC model predictive control |
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| 8 |
Improving Efficiency of Sampling-based Motion Planning via Message-Passing Monte Carlo |
通过消息传递蒙特卡洛方法提升采样基础运动规划效率 |
motion planning |
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| 9 |
Multi-Robot Motion Planning with Diffusion Models |
提出MMD算法,利用单机器人数据和扩散模型解决大规模多机器人运动规划问题 |
motion planning |
✅ |
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| 10 |
GAP-RL: Grasps As Points for RL Towards Dynamic Object Grasping |
提出GAP-RL框架,通过将抓取表示为点,实现动态环境中物体的强化学习抓取 |
manipulation sim-to-real reinforcement learning |
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| 11 |
Compact LED-Based Displacement Sensing for Robot Fingers |
提出一种紧凑型LED位移传感器,用于感知机器人手指的接触力与力矩。 |
manipulation |
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