以第一人称触觉和接近传感器作为观察先验用于双足机器人的碰撞避障
Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance
AI总结：
本文提出基于强化学习的双足机器人全身避障框架，通过躲避球任务验证传感器属性对避障行为的影响，发现接近传感器在传感范围充足时可替代显式物体定位，稀疏非定向信号在样本效率上优于密集定向信号。
摘要：
本研究提出了一种基于强化学习的双足机器人全身避障框架，通过躲避球任务验证了触觉和接近传感器的属性对避障行为的影响，发现当传感范围充足时，原始接近测量可以替代显式物体定位，稀疏非定向接近信号在样本效率上优于密集定向信号。
英文摘要：
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.
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。
