Robot Inner Attention Modeling for Task-Adaptive Teaming of Heterogeneous Multi Robots
This addresses the problem of efficient robot teaming for complex applications like disaster response, though it appears incremental as it builds on existing multi-agent reinforcement learning with a novel attention integration.
The paper tackles the challenge of dynamically composing heterogeneous multi-robot teams to meet varying task requirements while minimizing resource costs, and introduces an inner attention method that achieves high accuracy in flexible cooperation across different task scenarios.
Attracted by team scale and function diversity, a heterogeneous multi-robot system (HMRS), where multiple robots with different functions and numbers are coordinated to perform tasks, has been widely used for complex and large-scale scenarios, including disaster search and rescue, site surveillance, and social security. However, due to the variety of the task requirements, it is challenging to accurately compose a robot team with appropriate sizes and functions to dynamically satisfy task needs while limiting the robot resource cost to a low level. To solve this problem, in this paper, a novel adaptive cooperation method, inner attention (innerATT), is developed to flexibly team heterogeneous robots to execute tasks as task types and environment change. innerATT is designed by integrating a novel attention mechanism into a multi-agent actor-critic reinforcement learning architecture. With an attention mechanism, robot capability will be analyzed to flexibly form teams to meet task requirements. Scenarios with different task variety ("Single Task", "Double Task", and "Mixed Task") were designed. The effectiveness of the innerATT was validated by its accuracy in flexible cooperation.