Our lab performs research in the general areas of applied dynamical systems, robotics, and pattern recognition. The central theme of our research is to model and control complex systems comprising robotic, animal, and/or virtual agents. Applications of our work range from crowd management, environmental monitoring, quantifying animal behavior, and bioinspiration in robotic design and autonomy.

Virtual environment for studying crowd behavior and agent based models
Ground robot test bed for human-swarm interaction
Three-dimensional reconstruction of collective behavior in the wild
EEG headset for quantifying emotional and cognitive response to visual stimuli
Network reconstruction using model based and model-free methods. Applications in collective behavior and fault prediction in industrial robots
SPLASH is a hybrid platform that can fly and float for monitoring aquatic invasive species in disconnected water bodies. Designed and fabricated by Senior design team 2018
Subscope is a platform with a robotic arm monitoring aquatic invasive species. Designed and fabricated by Senior Design team 2019

Lab members

PI

Sachit Butail, cv, dissertation, google scholar, github

Current Graduate Students

  1. Rafal Krzysiak

  2. Malav Thakore

  3. Alexander Wills

  4. Arunim Bhattacharya

  5. Sathish V (IIIT-Delhi)

Current Interns/Undergraduate Students

  1. Daniel Meeks

  2. Abdullah Thahab

  3. Zachary Taylor

Past graduates

  1. Elham Mohammadi Jorjafki (ME MS 2018) thesis

  2. Kiran Maridi (ME MS 2018) thesis

  3. Hari Boddeeti (ME MS 2019) thesis

  4. Joseph Kempel (ME MS 2019) thesis

  5. Kate Chwistek (ME MS 2020) abstract

Projects (current)

Cues and actions for efficient nonverbal human-robot communication (2020-2022)

This project develops novel methods to advance human-robot intelligence through a series of experimental studies and rigorous mathematical analysis. The experiments involve tasks designed to exploit the strengths of robots and humans; robots are able to repetitively explore a large environment and humans have better awareness of the situation and domain expertise. The experimental tasks are inspired by the difficult problem of monitoring the vast number of invasive aquatic species threatening the Great Lakes region. The mathematical analysis is aimed at discovering effective robot actions in response to changes in human cognitive load, and efficient nonverbal interaction strategies between humans and robots. Research supported by National Science Foundation

Agent-based Modeling Toward Effective Testing and Contact-tracing During the COVID-19 Pandemic (2020-2021)

This project, carried out in collaboration with Dynamical Systems Laboratory in New York University, focuses on developing agent-based models to address social and mobility constraints as we respond to COVID19. The model will afford the simulation of critical what-if scenarios and will include the evaluation of different testing policies and mitigation actions, thereby constituting a valuable support to policy makers involved in the containment and eradication of the epidemic. Research supported by National Science Foundation

Multi-robot platform for environmental monitoring (2020-2021)

This project aims to enable hardware and virtual swarm robotic platforms for collaborative environmental monitoring. The robotic platform will consist of multiple ground robots that can seamlessly collaborate with a human through visual cues for monitoring structured environments. The virtual platform will simulate multiple UAVs that can be controlled by a human operator for monitoring unstructured environments. Research supported by NASA, Illinois Space Grant Consortium

Projects (Past)

Causal Relationships Underlying the Collective Dynamic Behavior of Swarms (2016-2019)

Living in groups affords several benefits for animals such as better feeding opportunities and reduced predation risks. In both instances-foraging and predator avoidance-critical information is transmitted nonverbally throughout the group, at different time scales. This project, carried out in collaboration with Dynamical Systems Laboratory, New York University, seeks to demonstrate that an information-theoretic approach can be used to measure social animal behavior. The research objective is to establish a rigorous model-free framework to study causal relationships in animal interactions validated by a series of hypothesis-driven experiments on zebra fish to emphasize unidirectional information transfer. Research supported by National Science Foundation through a sub-award from New York University, PI, Maurizio Porfiri

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