Learning to swarm with knowledge-based neural ordinary differential equations
A recent paper by members of the DCIST alliance uses the deep learning method, knowledge-based neural ordinary differential equations (KNODE) to develop a data-driven approach for extracting single-robot controllers from the observations of a swarm’s trajectory. The goal is to reproduce global swarm behavior using the extracted controller. Different from the previous works on imitation learning, this method does not require action data for training. The proposed method can combine existing knowledge about the single-robot dynamics, and incorporates information decentralization, time delay, and obstacle avoidance into a general model for controlling each individual robot in a swarm. The decentralized information structure and homogeneity assumption further allow the method for scalable training, i.e., the training time grows linearly with the swarm size. This method was applied on two different flocking swarms, in 2D and 3D respectively, and successfully reproduced global swarm behavior using the learnt controllers. In addition to the learning method, the paper also proposed the novel application of proper orthogonal decomposition (POD) for evaluating the performance of a learnt controller. Furthermore, extensive analysis on hyperparameters is conducted to provide more insights on the properties and characteristics of the proposed method.
Capability: T3C4C – Adaptive Swarm Behaviors for Uncertainty Mitigation (Hsieh)
Points of Contact: M. Ani Hsieh (PI) and Tom Z. Jiahao
Citation: T. Z. Jiahao, L. Pan, M. A. Hsieh “Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations.” Arxiv Preprint, December 2021.