论文标题
自然梯度共享控制
Natural Gradient Shared Control
论文作者
论文摘要
我们提出了共享控制的形式主义，这是定义融合用户控制和自主控制的策略的问题。共享自主系统提出的挑战是维护用户控制权限，同时允许机器人支持用户。这可以通过执行约束或在意图明确时最佳行动来完成。我们提出的解决方案依赖于从机器人和共享政策之间的差异约束中出现的自然梯度。我们通过对学习的机器人策略进行采样并计算本地梯度以在必要时增加用户控制来近似渔民信息。对操作任务进行的用户研究表明，我们的方法可以在对许多基线方法中保持控制权的效率完成。
We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control authority while allowing the robot to support the user. This can be done by enforcing constraints or acting optimally when the intent is clear. Our proposed solution relies on natural gradients emerging from the divergence constraint between the robot and the shared policy. We approximate the Fisher information by sampling a learned robot policy and computing the local gradient to augment the user control when necessary. A user study performed on a manipulation task demonstrates that our approach allows for more efficient task completion while keeping control authority against a number of baseline methods.
