论文标题
将先验知识纳入使用自主握把选择的软组织操纵的强化学习
Incorporating Prior Knowledge into Reinforcement Learning for Soft Tissue Manipulation with Autonomous Grasping Point Selection
论文作者
论文摘要
先前的软组织操纵研究假设已知抓地点并可以实现目标变形。在操作过程中，约束应该是恒定的，并且软组织周围没有障碍物。为了超越这些假设，在未知的约束下（例如Faffiia施加的力量）提出了一个具有先验知识的深入加强学习框架。先验知识是通过直观的操纵策略来表示的。作为代理的行动，使用调节因子来协调直觉方法和故意的网络。奖励功能旨在平衡探索和剥削的大变形。成功的仿真结果验证了所提出的框架可以操纵软组织，同时避免障碍物并增加新的位置限制。与软参与者（SAC）算法相比，所提出的框架可以加速训练程序并改善概括。
Previous soft tissue manipulation studies assumed that the grasping point was known and the target deformation can be achieved. During the operation, the constraints are supposed to be constant, and there is no obstacles around the soft tissue. To go beyond these assumptions, a deep reinforcement learning framework with prior knowledge is proposed for soft tissue manipulation under unknown constraints, such as the force applied by fascia. The prior knowledge is represented through an intuitive manipulation strategy. As an action of the agent, a regulator factor is used to coordinate the intuitive approach and the deliberate network. A reward function is designed to balance the exploration and exploitation for large deformation. Successful simulation results verify that the proposed framework can manipulate the soft tissue while avoiding obstacles and adding new position constraints. Compared with the soft actor-critic (SAC) algorithm, the proposed framework can accelerate the training procedure and improve the generalization.
