| 1 |
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning |
提出HGCR-DDPG算法,结合NMPC生成演示数据,提升机器人对可变形物体的操作能力。 |
manipulation model predictive control reinforcement learning |
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| 2 |
Learning Getting-Up Policies for Real-World Humanoid Robots |
提出一种两阶段学习框架,解决人形机器人在复杂地形下的跌倒恢复问题 |
humanoid humanoid robot humanoid locomotion |
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| 3 |
PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies |
PrivilegedDreamer:利用特权信息的显式想象实现策略的快速适应 |
manipulation sim-to-real domain randomization |
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| 4 |
IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation |
提出IMLE Policy,通过隐式最大似然估计实现快速且高效的视觉运动策略学习。 |
manipulation policy learning imitation learning |
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| 5 |
FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation |
FUNCTO:面向工具操作的函数中心单样本模仿学习 |
manipulation imitation learning |
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| 6 |
Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative Heterogeneous-Agent Reinforcement Learning |
提出对抗-合作异构智能体强化学习框架,提升灵巧双臂抓取能力 |
bi-manual reinforcement learning |
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| 7 |
HI-GVF: Shared Control based on Human-Influenced Guiding Vector Fields for Human-multi-robot Cooperation |
提出基于人影响导引向量场的共享控制方法,用于人-多机器人协作 |
teleoperation shared control |
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| 8 |
Towards Fusing Point Cloud and Visual Representations for Imitation Learning |
提出FPV-Net,融合点云与视觉表征,提升模仿学习操作任务性能 |
manipulation imitation learning |
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| 9 |
Soft Robotics for Search and Rescue: Advancements, Challenges, and Future Directions |
软体机器人助力搜寻与救援:综述现有技术并展望未来方向 |
locomotion |
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| 10 |
Improving Grip Stability Using Passive Compliant Microspine Arrays for Soft Robots in Unstructured Terrain |
提出被动柔顺微刺阵列,提升软体机器人在非结构化地形中的抓地稳定性 |
locomotion |
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