自然梯度共享控制
Natural Gradient Shared Control
摘要：
我们提出了一个形式主义的共享控制，这是定义一个政策，融合用户控制和自主控制的问题。共享自治系统带来的挑战是在允许机器人支持用户的同时保持用户控制权限。这可以通过强制约束或在意图明确时采取最佳行动来实现。我们提出的解决方案依赖于自然梯度出现的机器人和共享政策之间的分歧约束。我们近似的Fisher信息采样学习机器人的政策和计算局部梯度，以增加用户控制时，必要的。对操作任务进行的用户研究表明，我们的方法可以更有效地完成任务，同时保持对一些基线方法的控制权。
英文摘要：
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.
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。
