基于扭矩数据的接触估计算法，提升轮足机器人状态感知精度。
A Data-driven Contact Estimation Method for Wheeled-Biped Robots
- 用实测扭矩学习更新步骤，惯性数据预测状态变化
- 相比深度学习基线，性能更优且样本效率高
- 适合无专用传感器的新型轮足机器人使用
接触估计是四肢机器人的重要能力，接触的建立与中断直接影响状态估计和平衡控制。现有方法通常依赖门周期先验或专用接触传感器。本文针对新兴的轮足机器人类型（缺乏这些特性），设计了一种接触估计算法。该方法采用贝叶斯滤波框架，其中更新步骤通过真实机器人的力矩测量数据学习得到，预测步骤则依赖惯性测量。在大量真实机器人和仿真实验中验证了该方法的有效性。结果表明，该方法在性能上优于对比的深度学习基线，且样本效率显著更高。
原文摘要 · Abstract (English)
Contact estimation is a key ability for limbed robots, where making and breaking contacts has a direct impact on state estimation and balance control. Existing approaches typically rely on gate-cycle priors or designated contact sensors. We design a contact estimator that is suitable for the emerging wheeled-biped robot types that do not have these features. To this end, we propose a Bayes filter in which update steps are learned from real-robot torque measurements while prediction steps rely on inertial measurements. We evaluate this approach in extensive real-robot and simulation experiments. Our method achieves better performance while being considerably more sample efficient than a comparable deep-learning baseline.
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