Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection

• URL: http://arxiv.org/abs/2105.08601v1
• Date: Tue, 18 May 2021 15:32:07 GMT
• Title: Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
• Authors: Lifeng Zhou, Vishnu D. Sharma, Qingbiao Li, Amanda Prorok, Alejandro Ribeiro, Vijay Kumar
• Abstract summary: We focus on applications where robots are required to jointly select actions to maximize team submodular objectives. We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
• Score: 101.38634057635373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we develop a learning-based approach for decentralized submodular maximization. We focus on applications where robots are required to jointly select actions, e.g., motion primitives, to maximize team submodular objectives with local communications only. Such applications are essential for large-scale multi-robot coordination such as multi-robot motion planning for area coverage, environment exploration, and target tracking. But the current decentralized submodular maximization algorithms either require assumptions on the inter-robot communication or lose some suboptimal guarantees. In this work, we propose a general-purpose learning architecture towards submodular maximization at scale, with decentralized communications. Particularly, our learning architecture leverages a graph neural network (GNN) to capture local interactions of the robots and learns decentralized decision-making for the robots. We train the learning model by imitating an expert solution and implement the resulting model for decentralized action selection involving local observations and communications only. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots. The simulation results show our approach nearly matches the coverage performance of the expert algorithm, and yet runs several orders faster with more than 30 robots. The results also exhibit our approach's generalization capability in previously unseen scenarios, e.g., larger environments and larger networks of robots.

Related papers

• Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
  Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance. This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
  arXiv  Detail & Related papers  (2024-09-29T23:57:25Z)
• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [80.86089324742024]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem. CNN processes localized perception; a graph neural network (GNN) facilitates robot communications. Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• Asynchronous Perception-Action-Communication with Graph Neural Networks [93.58250297774728]
  Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments. The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
  arXiv  Detail & Related papers  (2023-09-18T21:20:50Z)
• Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
  We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN) Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
  arXiv  Detail & Related papers  (2021-03-08T21:48:55Z)
• Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments [10.345048137438623]
  We introduce a novel trajectory prediction model based on recurrent neural networks (RNN) We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance.
  arXiv  Detail & Related papers  (2021-02-10T11:11:08Z)
• Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning [12.988435681305281]
  Graph Neural Networks (GNNs) have become popular due to their ability to learn communication policies in decentralized multi-agent systems. We extend our previous work that utilizes GNNs in multi-agent path planning by incorporating a novel mechanism to allow for message-dependent attention. Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.
  arXiv  Detail & Related papers  (2020-11-26T10:37:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.