Multiple task optimization using dynamical movement primitives for whole-body reactive control

Whole-body controllers provide the tools to execute multiple simultaneous tasks on humanoid robots, but given the robot's internal and external constraints, interferences are often generated which impede task completion. Priorities can be assigned to each task to manage these interferences, unfortunately, this is often done at the detriment of one or more tasks. In this paper we present a novel framework for defining and optimizing multiple tasks in order to resolve potential interferences prior to task execution and remove the need for prioritization. Our framework parameterizes tasks with Dynamical Movement Primitives, simulates and evaluates their execution, and optimizes their parameters based on a general compatibility principle, which is independent of the robot's topology, tasks or environment. Two test cases on a simulation of a humanoid robot are used to demonstrate the successful optimization of initially interfering tasks using this framework.