论文标题
触觉力对自动学习的效用是任务依赖的
The utility of tactile force to autonomous learning of in-hand manipulation is task-dependent
论文作者
论文摘要
触觉传感器提供可用于学习和执行操纵任务的信息。但是，不同的任务可能需要不同级别的感官信息。反过来可能会影响学习率和表现。本文通过模拟的三指肌腱驱动的手评估了触觉信息对操纵自动学习的作用。我们比较了相同的学习算法（近端策略优化，PPO）学习两个具有三个触觉感应级别的操纵轴的操纵任务（在具有和不旋转旋转刚度的水平轴上滚动）的能力：没有传感，1D正常力量和3D力量矢量。令人惊讶的是，与最近没有感觉相比，与最近的操作相反，与没有传感相比，增加了一维力的学习率并不总是提高学习率 - 可能是由于正常力是否与任务相关。尽管如此，即使3D力感密度增加了感觉输入的维度 - 通常，这通常会导致更快的学习率和更好的性能。我们得出的结论是，通常，感觉输入仅在与任务相关时才有用 - 就像3D力对重力操纵的3D力感密度一样。此外，3D力传感的实用程序甚至可以用更高的感觉输入来抵消学习的增加计算成本。
Tactile sensors provide information that can be used to learn and execute manipulation tasks. Different tasks, however, might require different levels of sensory information; which in turn likely affect learning rates and performance. This paper evaluates the role of tactile information on autonomous learning of manipulation with a simulated 3-finger tendon-driven hand. We compare the ability of the same learning algorithm (Proximal Policy Optimization, PPO) to learn two manipulation tasks (rolling a ball about the horizontal axis with and without rotational stiffness) with three levels of tactile sensing: no sensing, 1D normal force, and 3D force vector. Surprisingly, and contrary to recent work on manipulation, adding 1D force-sensing did not always improve learning rates compared to no sensing---likely due to whether or not normal force is relevant to the task. Nonetheless, even though 3D force-sensing increases the dimensionality of the sensory input---which would in general hamper algorithm convergence---it resulted in faster learning rates and better performance. We conclude that, in general, sensory input is useful to learning only when it is relevant to the task---as is the case of 3D force-sensing for in-hand manipulation against gravity. Moreover, the utility of 3D force-sensing can even offset the added computational cost of learning with higher-dimensional sensory input.
