Bottom-Up Skill Discovery From Unsegmented Demonstrations for Long-Horizon Robot Manipulation

• Published in: IEEE robotics and automation letters Vol. 7; no. 2; pp. 4126 - 4133
• Main Authors: Zhu, Yifeng, Stone, Peter, Zhu, Yuke
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Cloning; Clustering; Deep learning for grasping and manipulation; Horizon; Image segmentation; imitation learning; Learning; Multitasking; Reinforcement learning; Robot sensing systems; Robots; sensorimotor learning; Skills; Structural hierarchy; Task analysis
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2022.3146589

We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demonstrations and use these skills to synthesize prolonged robot behaviors. Our method starts with constructing a hierarchica...