Multiform Adaptive Robot Skill Learning from Humans
This work addresses the challenge of flexible and intuitive robot skill acquisition for human-robot interaction, representing an incremental advancement over existing methods.
The paper tackles the problem of robotic manipulation by proposing a multiform learning approach that integrates adaptive learning from definition and evaluation, enabling robots to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control.
Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.