A recent paper by the members of the DCIST alliance studies how global dynamics and knowledge of high-level features can inform decision-making for robots in flow-like environments. Specifically, they investigate how coherent sets, an environmental feature found in these environments, inform robot awareness within these scenarios. The proposed approach is an online environmental feature generator which can be used for robot reasoning. They compute coherent sets online with techniques from machine learning and design frameworks for robot behavior that leverage coherent set features. The researchers demonstrate the effectiveness of online methods over offline methods. Notably, they apply these online methods for robot monitoring of pedestrian behaviors and robot navigation through water. Environmental features such as coherent sets provide rich context to robots for smarter, more efficient behavior.

Figure: Diagram of interplay between data, transfer operators, kernel methods, and environmental features. Transfer operators represent dynamical systems, where a state is lifted to a high-dimensional space and this lifting provides physical properties of the system. Many systems are defined by data exhibiting complex patterns, such as two nested rings, flows in oceans, taxi trajectories, and biological behaviors. Kernel methods transform this data to an alternative space with the use of kernel functions. Data is then easier to interpret, such as by separating two nested rings or by creating a Gram matrix for use in a kernel algorithm. Transfer operators are represented through kernel methods by embedding dynamical systems into a kernel space. Kernel algorithms extract environmental features from transfer operators, such as where humans tend to congregate in crowds, areas of gyres in oceans, or patterns of blood flow in hearts

Capability: T3C4B – Heterogeneous Multi-Robot Systems for Modeling and Prediction of Multiscale Spatiotemporal Processes

Points of Contact: M. Ani Hsieh (PI), Tahiya Salam, and Victoria Edwards

Video: https://youtu.be/D-4S6hnFr2E

Paper: https://arxiv.org/pdf/2109.06107.pdf

Citation: T. Salam, V. Edwards, M. A. Hsieh “Learning and Leveraging Environmental Features to Improve Robot Awareness.” arXiv preprint arXiv:2109.06107, September 2021.