Sachit Butail


Sachit Butail
Assistant Professor
Room B-102, IIIT-Delhi
New Delhi, India 110020
phone: +91 11-26907424
fax: +91 11-26907405

Bio: Sachit Butail received his Ph.D. in 2012 in Aerospace Engineering from University of Maryland, College park where his dissertation was on the motion reconstruction of animal groups using methods from estimation theory and computer vision. From 2012 to 2014, he was a postdoctoral fellow at the Dynamical Systems Laboratory at New York University where he worked on problems in collective behaviour and animal-robot interactions using methods from machine learning, time-series analysis, and information theory. His research interests are in the areas of dynamical systems, bioinspired autonomy, collective behaviour, and robotics. He currently directs the Natural and Engineered Collectives Research group in IIIT-Delhi.



Last updated on Nov 05, 2015



Journal articles

[1] N. Abaid, S. Butail, M. Porfiri, and D. Spinello. Dynamics of animal systems. European Physical Journal (Preface to Special Topics), 2015.
[2] T. Bartolini, S. Butail, and M. Porfiri. Temperature influences sociality and activity of freshwater fish. Environmental Biology of Fishes, 98(3):825-832, 2015. [ DOI ]
[3] T. Bartolini, V. Mwaffo, S. Butail, and M. Porfiri. Effect of acute ethanol administration on zebrafish tail beat motion. Alcohol, 49:721-725, 2015. [ DOI ]
[4] K. Gajamannage, S. Butail, M. Porfiri, and E. M. Bollt. Identifying manifolds underlying group motion in vicsek agents. The European Physical Journal (Special issue: Animal Dynamics), accepted, 2015.
[5] K. D. Gajamannage, S. Butail, M. Porfiri, and E. M. Bollt. Dimensionality Reduction of Collective Motion by Principal Manifolds. Physica D: Nonlinear Phenomena, 291:62-73, 2015. [ DOI ]
[6] F. Ladu, T. Bartolini, S. Panitz, F. Chiarotti, S. Butail, S. Macrì, and M. Porfiri. Live predators, robots, and computer-animated images elicit differential avoidance responses in zebrafish. Zebrafish (cover page), 12(3):205-214, 2015. [ DOI ]
[7] V. Mwaffo, R. P. Anderson, S. Butail, and M. Porfiri. A jump persistent turning walker to model zebrafish locomotion. Journal of the Royal Society Interface, 12(102):20140884, 2015. [ DOI ]
[8] V. Mwaffo, S. Butail, M. diBernardo, and M. Porfiri. Measuring zebrafish turning rate. Zebrafish, 12(3):250-254, 2015. [ DOI ]
[9] S. Butail, F. Ladu, D. Spinello, and M. Porfiri. Information flow in animal-robot interactions. Entropy (Special issue: Information in Dynamical Systems and Complex Systems), 16(3):1315-1330, 2014. [ DOI ]
[10] S. Butail, G. Polverino, P. Phamduy, F. Del Sette, and M. Porfiri. Influence of robotic shoal size, configuration, and activity on zebrafish behavior in a free-swimming environment. Behavioural Brain Research, 275:269-280, 2014. [ DOI ]
[11] S. Butail, P. Salerno, E. M. Bollt, and M. Porfiri. Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling. Behavior Research Methods, 2014. [ DOI ]
[12] A. Chicoli, S. Butail, Y. Lun, J. Bak-Coleman, S. Coombs, and D. A. Paley. The effects of flow on schooling Devario aequipinnatus: school structure, startle response and information transmission. Journal of Fish Biology, 84(5):1401-1421, 2014. [ DOI ]
[13] F. Ladu, S. Butail, S. Macrì, and M. Porfiri. Sociality modulates the effects of ethanol in zebrafish. Alcoholism, Clinical and Experimental Research, 38(7):2096-2104, 2014. [ DOI ]
[14] N. C. Manoukis, S. Butail, M. Diallo, J. M. C. Ribeiro, and D. A. Paley. Stereoscopic Video Analysis of Anopheles gambiae Behavior in the Field: Challenges and Opportunities. Acta Tropica, 132:S80-S85, 2014. [ DOI ]
[15] D. Shishika, N. C. Manoukis, S. Butail, and D. A. Paley. Male motion coordination in anopheline mating swarms. Scientific Reports, 4(6318), 2014. [ DOI ]
[16] S. Butail, T. Bartolini, and M. Porfiri. Collective response of zebrafish shoals to a free-swimming robotic fish. PLoS One, 8(10):e76123, 2013. [ DOI ]
[17] S. Butail, E. M. Bollt, and M. Porfiri. Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning. Journal of Theoretical Biology, 336(7):185-199, 2013. [ DOI ]
[18] S. Butail, N. C. Manoukis, M. Diallo, J. M. C. Ribeiro, and D. A. Paley. The dance of male Anopheles gambiae in wild mating swarms. Journal of Medical Entomology, 50(3):552-559, 2013. [ DOI ]
[19] S. Butail, N. C. Manoukis, M. Diallo, J. M. C. Ribeiro, T. Lehmann, and D. A. Paley. Reconstructing the flight kinematics of swarming and mating in wild mosquitoes. Journal of the Royal Society Interface, 9(75):2624-2638, 2012. [ DOI ]
[20] S. Butail and D. A. Paley. Three-dimensional reconstruction of the fast-start swimming kinematics of densely schooling fish. Journal of the Royal Society Interface, 9(66):77-88, 2011. [ DOI ]

Refereed conference proceedings

[1] V. Sathish, S. Ramaswamy, and S. Butail. A simulation based approach to detect wear in industrial robots. In Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), pages 1570-1575, Gothenberg, Sweden, August 2015.
[2] S. Butail. Simulating the effect of a social robot on moving pedestrian crowds. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2413-2418, Hamburg, Germany, 2015.
[3] S. Butail, T. Bartolini, and M. Porfiri. Collective response of zebrafish to a mobile robotic fish. In Proceedings of the ASME Dynamic Systems and Control Conference. Invited session on "Biologically-inspired control and its applications", page V001T07A001, Palo Alto, CA, October 2013. [ DOI ]
[4] S. Butail, A. Chicoli, and D. A. Paley. Putting the fish in the fish tank: Immersive VR for animal behavior experiments. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 5018-5023, Minneapolis, MN, USA, 2012. [ DOI ]
[5] S. Butail, N. C. Manoukis, M. Diallo, A. S. Yaro, A. Dao, S. F. Traoré, J. M. C. Ribeiro, T. Lehmann, and D. A. Paley. 3d tracking of mating events in wild swarms of the malaria mosquito Anopheles gambiae. In Proceedings of the IEEE Conference of Engineering in Medicine and Biology Society (EMBC), pages 720-723, Boston, MA, USA, January 2011. [ DOI ]
[6] S. Butail and D. A. Paley. 3d reconstruction of fish schooling kinematics from underwater video. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 2438-2443, Anchorage, AK, USA, May 2010. [ DOI ]
[7] S. Butail and D. A. Paley. Vision-based estimation of three-dimensional position and pose of multiple underwater vehicles. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2477-2482, St. Louis, MO, USA, 2009. [ DOI ]
[8] N. Sydney, S. Napora, S. Beal, P. Mohl, P. Nolan, S. Sherman, A. Leishman, S. Butail, and D. A. Paley. A Micro-UUV Testbed for Bio-Inspired Motion Coordination. In International Symposium Unmanned Untethered Submersible Technology, Durham, NH, USA, 2009.
[9] S. Butail and M. Peck. Non-Contacting Interfaces: A Case Study in Modular Spacecraft Design. In Proceedings of the Conference on Systems Engineering Research, volume 2, pages 27-34, New Jersey, NJ, USA, 2007. [ DOI ]


Motion Reconstruction of Animal Groups: From Schooling Fish to Swarming Mosquitoes (~14 MB)


Perceiving human crowds

With increased urbanization, human pedestrian crowds are a phenomena frequently witnessed in cities all over the world. In this context, the usefulness of robots in human societies will critically depend on their ability to actively navigate dense crowded situations. A first step in this direction is to automatically interpret crowd motion. This project seeks to develop and validate an algorithmic framework for the perception of dynamic environments. The research objective is to use methods in Bayesian estimation and statistical pattern recognition to robustly and reliably classify crowd motion from a level perspective.

Causal relationships underlying the collective dynamic behavior of swarms

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.

Natural and Engineered Collectives Laboratory

Our group performs research in the general areas of applied dynamical systems, robotics, and pattern recognition with problems themed around natural and engineered collectives. Our work is both experimental and theoretical in nature. In brief, we perform experiments with animals and/or robots, and then use the resulting data to develop and calibrate mathematical models; insights from the models give rise to more hypotheses. Our research relies heavily on collaborations with biologists, mathematicians, and engineers. We seek to contribute both to the fields of autonomous systems by drawing inspiration from animals, as well as animal behavior by developing and applying new tools from dynamical systems analysis.

Graduate students

  1. Sathish V

Interns/Undergraduate students

  1. Abhishek Bhatia
  2. Puneet Jain
  3. Naman Gupta
  4. Anmol Singh
  5. Sidharth Raja


Modeling complex systems Monsoon 2014, 2015
Math III (co-taught) Monsoon 2014
Stochastic estimation and control Winter 2015