Composable Causality in Semantic Robot Programming

Abstract

Assembly tasks are challenging for robot manipulation because the robot must reason over the composed effects of actions and execute multi-objective behaviors. Robots typically use predefined priorities provided by users to determine how to compose controller behaviors, but we want the robot to autonomously select these compositions based on their composed effects within the task. We present Composable Causality in Semantic Robot Programming to allow robots to reason over the composed effects of controllers when executing multi-objective actions and autonomously compose controllers without predefined priorities. Our proposed causal control basis combines controller behaviors with causal information about how the behaviors can be used to execute high-level symbolic actions. The robot uses the causal control basis to predict the transition probability of achieving the composed effects of a multi-objective action. The composed causality estimates are used to select which action to execute within the context of a furniture assembly task. We evaluate the robot's transition probability estimates in different furniture assembly trials in simulation on the Baxter robot. The robot's ability to assemble furniture using different multi-objective connection actions demonstrates the usefulness of the composed causality estimates from our causal control basis.