In this thesis, we are particularly interested in investigating machine learning (especially graph neural network) based approaches to find the trade-off between optimality and complexity by offloading online computation into an offline training process. Yet, learning-based methods also yield the need for sim-to-real systems and solutions to minimize the gap and provide interpretability and guarantee for the generated solutions.
Hence, we first developed a framework that can learn to communicate between robots based on Graph Neural Networks (GNNs) towards better individual decision-making given its local information in a decentralized manner. This framework is composed of an encoder (i.e. Convolutional Neural Network) that extracts adequate features from local observations, and a GNN that learns to explicitly communicate these features among robots, and Multilayer Perceptron for action selection. By jointly training these components, the system can learn to determine best what information is relevant for the team as a whole and share this to facilitate efficient path planning. Following up with that, we propose Message Aware Graph Attention neTwork (MAGAT) to combine a GNN with a key-query-like attention mechanism to improve the effectiveness of inter-robot communication. We demonstrate the generalizability of our model by training the model on small problem instances and testing it on increasing robot density, varying map size, and much larger problem instances (up to~\times 100 the number of robots).

To port our solution into the real world, we developed a ROS-based system that allows for the fully decentralized execution of GNN-based policies.We demonstrated our framework on a case study that requires tight coordination between robots, and presented first-of-a-kind results that showed successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on ad-hoc communication. Extending this system, we proposed a vision-only-based learning approach that leverages a GNN to encode and communicate relevant viewpoint information to the mobile robots. During navigation, the robot is guided by a model that we train through imitation learning to approximate optimal motion primitives, thereby predicting the effective cost-to-go (to the target). Our experiments demonstrated its generalizability in guiding robots in previously unseen environments with various sensor layouts.

Vanilla GNN-based decentralized path planning has demonstrated its performance empirically via an end-to-end learning approach. However, these black box approaches are facing challenges to directly deploy in the actual workplace, as they are hard to find a guaranteed and interpretable solution. Therefore, we designed Graph Transformer, as a heuristic function, to accelerate the focal search within Conflict-Based Search (CBS) in a non-grid setting, especially dense graphs. Our framework guarantees both the completeness and bounded suboptimality of the solution.

For the explainability and interpretability for RL, we introduced a global path planning algorithm (for example, A*) to generate a globally optimal path, which act as part of the reward function to encourage the robot to explore all potential solutions `weekly supervised' by the optimal path. As our reward function is independent of the environment, our trained framework generalizes to arbitrary environments and can be used to solve the multi-robot path planning problem in a fully distributed reactive manner.

Throughout my Ph.D. research, I first proposed communication-aware motion planning for multi-robot coordination, where GNNs are introduced to build communication channels for multi-robot teams so that they can learn how to communicate with each other explicitly. The feasibility of this novel research idea has been validated by various simulation experiments based on an end-to-end imitation learning pipeline. To port them into reality, we built a ROS2-based system with adhoc communication to demonstrate our idea in a multi-robot passage scenario and single-robot navigation assisted by randomly sampled camera-based sensors in an unknown environment. Finally, we developed methods that provide interpretation and performance guarantees in the previous black box approaches by introducing a heuristic function into the focal search of CBS and designing a novel reward mechanism called G2RL.