PICO: Primitive Imitation for COntrol
This addresses the challenge of flexible and efficient control in robotics, though it appears incremental as it combines existing ideas like imitation learning and task decomposition.
The paper tackles the problem of generalizing from demonstrations to new behaviors in complex control systems by decomposing demonstrations into sub-behaviors and dynamically blending primitives, with results showing PICO can detect novel behaviors and build missing control policies on two robotic platforms.
In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO. The approach combines ideas from imitation learning, task decomposition, and novel task sequencing to generalize from demonstrations to new behaviors. Demonstrations are automatically decomposed into existing or missing sub-behaviors which allows the framework to identify novel behaviors while not duplicating existing behaviors. Generalization to new tasks is achieved through dynamic blending of behavior primitives. We evaluated the approach using demonstrations from two different robotic platforms. The experimental results show that PICO is able to detect the presence of a novel behavior primitive and build the missing control policy.