Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies

With advanced development in robotics and autonomous systems in the past decades, the opportunities and demands for more complex physical human-robot interaction (pHRI) in our everyday unconstrained environments are rising; thus, it is critical for robots to be adaptive, compliant, reactive, safe and easy to program [1, 2, 3]. In many cases, robots will need to acquire new skills to satisfy task requirements in an ever-changing environment. It is usually difficult for non-experts to program robots for complex motion tasks and even tedious for experts to reprogram them when task requirements change. A straightforward and intuitive approach for robots to develop new skills is through Learning from Demonstration (LfD) [4, 5, 6, 7, 8]. This paradigm allows robots to acquire skills, typically encoded or defined in literature as action policies, motion policies, or imitation policies, directly from motion examples provided by humans or even other robots, mirroring a teacher-student relationship. In recent years, significant progress has been made in using LfD to learn complex and diverse motion tasks.