An Integrated System for Perception-Driven Autonomy with Modular Robots
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Abstract (arXiv)
The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware, and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task. The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3.
5 papers.
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- 2023Towards Knowledge-driven Autonomous Drivingcited by 29
- 2021Learning Modular Robot Control Policiescited by 18
- 2022REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfercited by 12
- 2023Towards Knowledge-driven Autonomous Driving7 Dec 2023
- 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving4 Oct 2023
- 2023Drive Like a Human: Rethinking Autonomous Driving with Large Language Models14 Jul 2023
- 2022REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer10 Feb 2022
- 2021Learning Modular Robot Control Policies20 May 2021