Learning and Sequencing of Object-Centric Manipulation Skills for Industrial Tasks
This work addresses the problem of rapid skill learning and sequencing for industrial robots, representing an incremental improvement with a novel method for known bottlenecks.
The paper tackles the challenge of enabling robots to quickly learn flexible manipulation skills that adapt to workspace configurations and can be sequenced for complex tasks, resulting in a framework that significantly reduces manual modeling efforts while ensuring high flexibility and reusability of skills.
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation tasks, robots should be able to sequence several skills and adapt them to changing situations. In this work, we propose a rapid robot skill-sequencing algorithm, where the skills are encoded by object-centric hidden semi-Markov models. The learned skill models can encode multimodal (temporal and spatial) trajectory distributions. This approach significantly reduces manual modeling efforts, while ensuring a high degree of flexibility and re-usability of learned skills. Given a task goal and a set of generic skills, our framework computes smooth transitions between skill instances. To compute the corresponding optimal end-effector trajectory in task space we rely on Riemannian optimal controller. We demonstrate this approach on a 7 DoF robot arm for industrial assembly tasks.