Collision-free motion is often aided by tactile and proximity sensors distributed on the body of the robot due to their resistance to occlusion as opposed to external cameras. However, how to shape the sensor's properties, such as sensing coverage; type; and range, to enable avoidant behavior remains unclear. In this work, we present a reinforcement learning framework for whole-body collision avoidance on a humanoid H1-2 robot and use it to characterize how sensor properties shape learned avoidance behavior. Using dodgeball as a benchmark task, we ablate the properties of sensors distributed across the upper body of the robot and find that raw proximity measurements can substitute for explicit object localization provided the sensing range is sufficient and that sparse non-directional proximity signals outpace dense directional alternatives in sample efficiency.

翻译：碰撞避免运动通常依赖分布在机器人身体上的触觉和近距传感器，因其不易被遮挡，优于外部相机。然而，如何设计传感器的特性，例如感知覆盖范围、类型和探测距离，以实现避障行为仍不明确。本文提出一种用于人形机器人H1-2全身碰撞避免的强化学习框架，并利用该框架刻画传感器特性如何影响习得的避障行为。以躲避球为基准任务，我们对机器人上半身分布的传感器特性进行消融实验，发现当探测距离足够时，原始近距测量可替代显式目标定位，且稀疏非定向近距信号在样本效率上优于密集定向方案。