Plan Explicability and Predictability for Robot Task Planning

TL;DR

This paper introduces the concepts of plan explicability and predictability for robot task planning, proposing a learning-based approach to generate plans that are easier for humans to understand and anticipate, thereby improving human-robot interaction safety and efficiency.

Contribution

It presents a novel framework for quantifying and synthesizing human-understandable robot plans using learned labeling schemes, addressing a gap in high-level task planning for social robots.

Findings

Plans labeled with the proposed method are more understandable to humans.

Robots using explicable and predictable plans show improved human-robot interaction.

The approach is effective in both synthetic and real-world robot experiments.

Abstract

Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan {\it explicability} and {\it predictability}. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach