Stable Motion Primitives via Imitation and Contrastive Learning

Understand

Learning from humans allows non-experts to program robots with ease, lowering the resources required to build complex robotic solutions.

• Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents.
• In this work, we focus on reaching/point-to-point motions, where robots must always reach their goal, independently of their initial state.
• This can be achieved by modeling motions as dynamical systems and ensuring that they are globally asymptotically stable.