COOPERA: Continual Open-Ended Human-Robot Assistance

2025-10-27

AI Summary

Current robotic assistants struggle to adapt to individual users’ psychological traits, long-term intentions, and behavioral habits, remaining largely confined to predefined tasks in structured environments and lacking mechanisms for sustained personalized learning. To address this, we propose the first unified framework enabling cross-temporal-scale, open-ended human–robot collaboration. Our method introduces: (1) a psychology-driven, evolvable human model that jointly encodes stable personality traits and dynamic intention inference; (2) a benchmark and learning-based approach for personalized collaboration policy acquisition, integrating continual feedback, context-aware intention modeling, and adaptive machine learning; and (3) empirical validation in a multi-task simulation environment featuring ecologically valid human behavioral modeling. Results demonstrate significant improvements in long-term collaboration stability and task completion rates, attributable to accurate intention inference and effective personalization.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly 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/

Problem

• Robots lack human trait modeling for long-term collaboration
• Existing systems focus on predefined tasks in structured environments
• Need personalized robot actions based on human traits and intents

Innovation

• Simulated humans with psychological traits drive interactions
• Personalizes robot actions by learning human traits
• Integrates continuous feedback for long-term collaboration