COOPERA: Continual Open-Ended Human-Robot Assistance

TL;DR

COOPERA is a framework that enables long-term, open-ended human-robot collaboration by modeling human traits and intentions, allowing robots to personalize assistance through continuous feedback and interaction in complex environments.

Contribution

This work introduces COOPERA, the first framework for continual, open-ended human-robot assistance that models human traits and long-term intentions for personalized collaboration.

Findings

Simulated humans reflect realistic behaviors.

Personalization of robot actions improves collaboration.

Framework supports study of long-term human-robot interactions.

Abstract

To understand and collaborate with humans, robots must account for individual human traits, habits, and activities over time. However, most robotic assistants lack these abilities, as they primarily focus on predefined tasks in structured environments and lack a human model to learn from. This work introduces COOPERA, a novel framework for COntinual, OPen-Ended human-Robot Assistance, where simulated humans, driven by psychological traits and long-term intentions, interact with robots in complex environments. By integrating continuous human feedback, our framework, for the first time, enables the study of long-term, open-ended human-robot collaboration (HRC) in different collaborative tasks across various time-scales. Within COOPERA, we introduce a benchmark and an approach to personalize the robot's collaborative actions by learning human traits and context-dependent intents. Experiments validate the extent to which our simulated humans reflect realistic human behaviors and demonstrate the value of inferring and personalizing to human intents for open-ended and long-term HRC. Project Page: https://dannymcy.github.io/coopera/