Plan Explicability and Predictability for Robot Task Planning (1511.08158v2)

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

• The paper introduces concepts of plan explicability and predictability, proposing that robots select or synthesize task plans that align with human expectations to improve human-robot interaction.
• Researchers employ Conditional Random Fields (CRFs) to learn human interpretations of robot action sequences, enabling robots to calculate how understandable and predictable their plans are to people.
• Evaluations demonstrated that plans generated using this method are significantly more explicable to humans compared to traditional cost-optimizing planners, fostering better trust and efficiency in human-robot collaboration.

Plan Explicability and Predictability in \Robot Task Planning

The paper "Plan Explicability and Predictability for Robot Task Planning," authored by Yu Zhang et al., addresses a critical aspect of intelligent robot operation in human-populated environments—ensuring that autonomous agents generate task plans that are comprehensible and predictable to humans. As robots increasingly interact and coexist with humans, the importance of this capability cannot be overstated, not only for enhancing human-robot cooperation but also for mitigating cognitive load and potential safety risks.

To approach these issues, the paper introduces two key concepts: plan explicability and predictability. These measures allow robots to choose and synthesize task plans that align closely with human expectations, making the robots’ actions understandable and their future actions predictably aligned with human anticipation. The methodology rests on the assumption that humans interpret robot plans by associating tasks with the robot’s sequential actions, a process conceptualized as labeling.

The researchers employ Conditional Random Fields (CRFs) to learn the human labeling scheme for robot actions from training examples—essentially capturing how humans map robot actions to tasks. This labeled understanding enables robots to calculate the explicability and predictability of new plans, thus allowing them to proactively select or synthesize plans that better match human expectations.

The paper utilizes a systematic approach to evaluate the efficacy of their proposals, both through synthetic domains and physical robot interactions. Through a synthetic rover domain, they assess the model's ability to generate explicable and predictable plans and demonstrate significant performance under varying conditions and levels of task complexity or noise. Additionally, human subject evaluations with physical robots in a blocks world domain reaffirmed that plans generated via the authors' methodology are notably more explicable to humans than those generated by traditional cost-optimizing planners.

The implications of this research are profound, particularly as robots begin to perform more complex tasks and integrate into everyday human environments. By synthesizing more explicable and predictable plans, the interaction between humans and robots can become more seamless and safer, fostering greater trust and efficiency. This research not only enhances immediate human-robot collaboration but also potentially contributes to future developments in AI where interpretability and predictability are paramount—even beyond interactions with humans.

Looking forward, this approach offers fertile ground for application in other domains, including those requiring unpredictability—such as in defense scenarios where plan unpredictability can be leveraged against adversarial tactics. By reversing the goal from maximizing to minimizing explicability and predictability, researchers can explore divergent applications that require keeping intentions veiled.

In summary, Zhang et al.'s formulation of plan explicability and predictability presents a sophisticated framework for improving the interaction between autonomous robots and humans. It marks a significant step towards realizing intuitive and safe robot behavior in complex shared environments, while maintaining a robust foundation for future explorations in AI and autonomous planning.