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Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots

arXiv 2410.06372 ↗First posted 8 Oct 2024cs.RO cs.AIcited by 0

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Abstract (arXiv)

Cooperative mission planning for heterogeneous teams of mobile robots presents a unique set of challenges, particularly when operating under communication constraints and limited computational resources. To address these challenges, we propose the Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework, which leverages multi-agent reinforcement learning (MARL) to coordinate distributed decision making among agents with diverse sensing, motion, and actuation capabilities, operating under sporadic ad hoc communication. A Class-based Macro-Action Decentralized Partially Observable Markov Decision Process (CMacDec-POMDP) is also formulated to effectively model asynchronous decision-making for heterogeneous teams of agents. The framework utilizes an asynchronous centralized training and distributed execution scheme, enabled by the proposed Asynchronous Multi-Agent Transformer (AMAT) architecture. This design allows a single trained model to generalize to larger environments and accommodate varying team sizes and compositions. We evaluate CATMiP in a 2D grid-world simulation environment and compare its performance against planning-based exploration methods. Results demonstrate CATMiP's superior efficiency, scalability, and robustness to communication dropouts and input noise, highlighting its potential for real-world heterogeneous mobile robot systems. The code is available at https://github.com/mylad13/CATMiP

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 62 references that are in the map. First posted on arXiv 8 Oct 2024.

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References · 27

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15 more
  1. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 160
  2. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 153
  3. 2023A Survey on Transformers in Reinforcement Learningcited by 50
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