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An End-To-End System for Accomplishing Tasks with Modular Robots

Abstract

The advantage of modular robot systems lies in their flexibility, but this advantage can only be realized if there exists some reliable, effective way of generating configurations (shapes) and behaviors (controlling programs) appropriate for a given task. In this paper, we present an end-to-end system for addressing tasks with modular robots, and demonstrate that it is capable of accomplishing challenging multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A large design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) modular robot hardware. The broader goal of this project is enabling users to address real-world tasks using modular robots. We believe this work represents an important step toward this larger goal.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🧭 Keyword Pioneer — task planning
🐝 Cross-Pollinator — Artificial Intelligence, Machine Learning, Natural Language Processing, Reinforcement Learning
🐣 Hot Topic Early Bird — task planning