Research at the ARCS Lab focuses on the intersection of dynamical systems, robotics, control, and machine learning. Current work investigates (i) resilient (semi-)autonomous multi-robot planning under uncertainty, (ii) stochastic control and dynamical systems theory, (iii) robust legged locomotion at small scales, and (iv) learning-based mobile robot navigation.

The lab has 5 active research awards from NSF, DARPA, and ONR, as well as 2 active equipment awards from ARL and ONR. These awards support 7 Ph.D. students in the group, as well as the procurement of all necessary equipment and supplies to conduct our research. Information on (selected) current and past projects listed below aims to offer a high-level illustration of our research agenda. For more detailed information please check our technical publications and/or contact us!

Current Projects

Autonomous Multi-Robot Visual Monitoring for Urban, Agricultural, and Natural Resource Management

The project addresses fundamental research to develop novel autonomous and adaptive monitoring systems for natural resources across large spatio-temporal scales using networks of aerial robots equipped with visual sensors. The robots will be able to autonomously adapt to the observed phenomena and to multiple, often conflicting, time-varying constraints and mission specifications, greatly improving the precision of the collected data, and allowing spatio-temporal scalability. This is achieved by considering tradeoffs between visual sensing, real-time trajectory planning, decision making, and system optimization.

Key research topics addressed by ARCS:

  • Multi-robot information gathering
  • Multi-agent decision making
  • Field testing and evaluation


Efficient Collaborative Perception over Controllable Agent Networks

The goal of this project is to create the knowledge to facilitate effective and efficient collaborative perception on top of a set of independent and multi-modal data generating agents. The project will study how to jointly model social and sensor data and use this modeling to efficiently support spatio-temporal queries on the joint embedding space. In addition to mapping information from multi-modal disparate sources to a common information space, this project will study how to optimize the attention routing of controllable agents like UAVs to maximize the reliability and coverage of the collected information.

Key research topics addressed by ARCS:

  • Multi-robot dynamic task allocation
  • Coordinated active sensing
  • Prototype development and evaluation


Extracting Dynamics from Limited Data for Modeling and Control of Unmanned Autonomous Systems

This project investigates the mechanisms that uncertainties in robot-environment interactions affect (small) robot behavior. Small robot motion is more stochastic since errors at the actuators and uncertain interactions with the environment amplify errors in pose. The goal is to introduce a platform-agnostic, data-driven modeling framework to quantify uncertainty and subsequently exploit it via control for reliable robot navigation under uncertainty. The specific aims are to: 1) extract dynamics using limited data for modeling uncertain systems; 2) synthesize uncertainty-aware model-based controllers based on derived reduced-order models; and 3) test and validate theoretical analysis and derived models and control algorithms with aerial, ground, and marine robots. Spectral methods are used to extract spatio-temporal dynamics and to quantify uncertainty. A model-reference adaptive control scheme utilizes extracted dynamics and uncertainty for reliable robot navigation. While the basic principles developed in this research are grounded on small robots, this project's findings may generalize to larger robots with limited sensing and noisy actuation.