ヒューマノイドの接触状態推定と制御のための教師なし接触学習
Unsupervised Contact Learning for Humanoid Estimation and Control
固有感覚センサのみを用いた教師なしファジィクラスタリングで6自由度の接触確率を推定し、脚の滑りや回転がある不整地での歩行においてベース状態推定の精度を向上させた研究。
著者: Nicholas Rotella, Stefan Schaal, Ludovic Righetti
分類: cs.RO
原文アブストラクト
This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.
