手持灵活工具的灵活机器人手操作与仿真到现实迁移
In-Hand Manipulation of Articulated Tools with Dexterous Robot Hands with Sim-to-Real Transfer
AI总结：
本文提出了一种通过传感器驱动改进和交叉注意机制，实现灵活机器人手在现实中的关节工具操作，提高了仿真到现实迁移的鲁棒性和泛化能力。
摘要：
强化学习（RL）和仿真到现实迁移已经推进了刚性物体的操作。然而，由于接触丰富的动态要求同时稳定的抓取和自由的手持操作，对于关节机构来说，策略仍然脆弱。此外，实际生活中，关节装置和机器人手表现出未建模的关节现象，如摩擦、卡死和回差，这会增加仿真到现实的差距，且机器人手在触觉感知方面仍无法达到理想状态，无论是覆盖范围、灵敏度还是特异性。在本文中，我们提出了一种学习灵活手持操作关节工具的原始方法，其关节操作和运动学冗余性相对于人类手较低。我们的方法在仿真训练的基础策略上增加了由传感器驱动的改进，该改进通过硬件演示学习。这种改进基于本体感觉和目标关节状态，通过交叉注意融合整个手的触觉和力矩反馈与策略的动作意图。所得到的控制器能够在线适应实例特定的关节属性，稳定接触交互，并在扰动下调节内部力。我们验证了我们的方法在多样化的现实工具上的有效性，包括剪刀、钳子、微创手术器械和夹合器，展示了稳健的仿真到现实迁移、改进的扰动鲁棒性和在结构相关关节工具上的泛化能力，而无需精确的物理建模。
英文摘要：
Reinforcement learning (RL) and sim-to-real transfer have advanced rigid-object manipulation. However, policies remain brittle for articulated mechanisms due to contact-rich dynamics that require both stable grasping and simultaneous free in-hand articulation. Furthermore, articulated objects and robot hands exhibit under-modeled joint phenomena such as friction, stiction, and backlash in real life that can increase the sim-to-real gap, and robot hands still fall short of idealized tactile sensing, both in terms of coverage, sensitivity, and specificity. In this paper, we present an original approach to learning dexterous in-hand manipulation of articulated tools that has reduced articulation and kinematic redundancy relative to the human hand. Our approach augments a simulation-trained base policy with a sensor-driven refinement learned from hardware demonstrations. This refinement conditions on proprioception and target articulation states while fusing whole-hand tactile and force-torque feedback with the policy's action intent through cross-attention. The resulting controller adapts online to instance-specific articulation properties, stabilizes contact interactions, and regulates internal forces under perturbations. We validate our method across diverse real-world tools, including scissors, pliers, minimally invasive surgical instruments, and staplers, demonstrating robust sim-to-real transfer, improved disturbance resilience, and generalization across structurally related articulated tools without precise physical modeling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
