Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation

TL;DR

This paper introduces a regret-based reactive synthesis framework for robotic manipulation that balances task guarantees with cooperative behavior by considering human intentions, improving robot-human interaction quality.

Contribution

It relaxes the adversarial assumption in reactive synthesis by incorporating regret minimization, enabling robots to seek cooperation while ensuring task completion.

Findings

Framework effectively balances cooperation and task guarantees.

Case studies demonstrate improved human-robot interaction.

Regret minimization leads to more friendly robot behaviors.

Abstract

As robots gain capabilities to enter our human-centric world, they require formalism and algorithms that enable smart and efficient interactions. This is challenging, especially for robotic manipulators with complex tasks that may require collaboration with humans. Prior works approach this problem through reactive synthesis and generate strategies for the robot that guarantee task completion by assuming an adversarial human. While this assumption gives a sound solution, it leads to an "unfriendly" robot that is agnostic to the human intentions. We relax this assumption by formulating the problem using the notion of regret. We identify an appropriate definition for regret and develop regret-minimizing synthesis framework that enables the robot to seek cooperation when possible while preserving task completion guarantees. We illustrate the efficacy of our framework via various case studies.