Generating Reactive Approach Motions Towards Allowable Manifolds using Generalized Trajectories from Demonstrations

摘要

There is a high cost associated to the time and expertise required to program complex robot applications with high variability. This is one of the main barriers that inhibit the entry of robotic automation in small and medium-sized enterprises. To tackle the high level of task uncertainty associated with changing conditions of the environment, we propose a framework that leverages a combination between learning from demonstration (LfD) and constraint-based task specification and control. This synergy enables our framework to use LfD to generalize reactive approach motions (RAMo) towards not only a single pose but towards an allowable manifold defined with respect to the object to interact with. As a result, the robot executes the task by following a feasible approach motion gen-eralized from the learned information. This approach motion is generated based on an initial representation of the environment, and it can be reactively adapted in function of current updates of the environment using sensor information. The proposed framework enables the system to deal with applications that involve a high level of uncertainty, increasing the flexibility and robustness, compared to traditional sense-plan-act paradigms.