用触觉与接近传感器提升人形机器人避障能力
Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance
- 基于强化学习构建全身避障框架，测试不同传感器配置
- 远距离非方向性信号比密集方向性信号更高效
- 接近感应足够时可替代物体定位，适合真实场景部署
由于不受遮挡影响，安装在机器人身体上的触觉和接近传感器常被用于辅助无碰撞运动。然而，如何设计传感器的感知范围、类型和分布以实现有效避障仍不明确。本文针对人形机器人H1-2，提出一种全身碰撞规避的强化学习框架，并以躲避球任务为基准，系统分析上半身传感器配置的影响。结果表明：当感知范围充足时，原始接近测量可替代显式物体定位；而稀疏的非方向性接近信号在样本效率上优于密集的方向性信号。
原文摘要 · Abstract (English)
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.
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