Cross-robot behavior adaptation through intention alignment

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Imitation learning (IL) enables robots to learn tasks from demonstrations.
• Conventional IL struggles with adaptability due to strict requirements for identical conditions and robot embodiments.
• Generalization across diverse robots and environments remains a significant challenge in current IL.

Purpose of the Study:

• To introduce the Intention-Aligned Imitation Learning (IAIL) framework for enhanced robot behavior adaptation.
• To enable robots to reproduce motions from heterogeneous peers across unseen situations.
• To facilitate scalable task allocation in heterogeneous robot teams.

Main Methods:

• Developed IAIL, a novel behavior adaptation approach inspired by human cultural learning.
• Constructed a shared intention space connecting robot motions with natural language annotations.
• Enabled inference-time behavior adaptation across diverse embodiments and environmental contexts.

Main Results:

• Validated IAIL through real-world experiments with seven distinct robots in multistep collaboration tasks.
• Demonstrated robust intention-aligned behavior adaptation across variations in embodiment, motion modality, and task configuration.
• Showcased flexible behavior transfer and resilient multirobot systems for reliable collaboration.

Conclusions:

• IAIL significantly extends the scope of imitation learning beyond direct skill mapping.
• The framework supports adaptable and generalizable robot behaviors in heterogeneous teams.
• IAIL contributes to the development of resilient, autonomous multirobot systems for real-world applications.