2018/10/03 by Alban Laflaquière, A. V. Terekhov, Laflaquière, Alban +7
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Human Motion and Animation #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Robot Manipulation and Learning #Robotics (cs.RO) #Time Series Analysis and Forecasting #cs.LG #cs.RO #stat.ML
paper · pdf · doi:10.48550/arxiv.1810.01866
6 pages, 3 figures, IROS 2013
arxiv created 2018/10/03 · openalex publication_date 2018/10/03 · arxiv updated 2018/10/05 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.