2021/01/28 by Chen Wang, Shaoxiong Wang, Wang, Chen +8 · 5 citations
Computer Science · Engineering · Neuroscience · #Advanced Sensor and Energy Harvesting Materials #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robot Manipulation and Learning #Robotics (cs.RO) #Tactile and Sensory Interactions #cs.AI #cs.CV #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.2101.11812
IROS 2020
arxiv created 2021/01/28 · openalex publication_date 2021/01/28 · arxiv updated 2021/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Several robot manipulation tasks are extremely sensitive to variations of the physical properties of the manipulated objects. One such task is manipulating objects by using gravity or arm accelerations, increasing the importance of mass, center of mass, and friction information. We present SwingBot, a robot that is able to learn the physical features of a held object through tactile exploration. Two exploration actions (tilting and shaking) provide the tactile information used to create a physical feature embedding space. With this embedding, SwingBot is able to predict the swing angle achieved by a robot performing dynamic swing-up manipulations on a previously unseen object. Using these predictions, it is able to search for the optimal control parameters for a desired swing-up angle. We show that with the learned physical features our end-to-end self-supervised learning pipeline is able to substantially improve the accuracy of swinging up unseen objects. We also show that objects with similar dynamics are closer to each other on the embedding space and that the embedding can be disentangled into values of specific physical properties.