Designing Environments Conducive to Interpretable Robot Behavior

TL;DR

This paper explores how designing structured environments can enhance the interpretability of robot behavior, making human-robot collaboration more effective by shaping expectations and reducing behavior costs.

Contribution

It introduces a novel environment design framework that considers multiple tasks and time horizons to promote explicable robot behavior.

Findings

Environment design can improve interpretability of robot actions.

Trade-offs exist between design costs and behavior costs over time.

Structured environments facilitate more predictable robot behavior.

Abstract

Designing robots capable of generating interpretable behavior is a prerequisite for achieving effective human-robot collaboration. This means that the robots need to be capable of generating behavior that aligns with human expectations and, when required, provide explanations to the humans in the loop. However, exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit the expected behavior. Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior. In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a type of interpretable behavior -- known in the literature as explicable behavior. We formulate a novel environment design framework that considers design over multiple tasks and over a time horizon. In addition, we explore the longitudinal aspect of explicable behavior and the trade-off that arises between the cost of design and the cost of generating explicable behavior over a time horizon.