Anthony Simeonov

I am a first-year Ph.D. student in the EECS department at MIT, advised by Professor Alberto Rodriguez and Professor Pulkit Agrawal. I'm interested in robot manipulation and physical interaction. I am fortunate to be supported by the NSF GRFP.

Previously, I received my B.S. in Mechanical Engineering from UC San Diego, where I worked with Professor Michael Yip. I have also spent time as a research intern at Disney Research Los Angeles.

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I'm currently interested in machine learning for perception-driven planning and control, applied to problems in robot manipulation and physical interaction. The goal is to equip robots with reusable manipulation skills, through a combination of effective perceptual representations and reactive behaviors. Previously, I have studied the design, modeling, and control of inherently compliant artificial muscle actuators.


Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
Ahmed Qureshi, Yinglong Miao, Anthony Simeonov, Michael C. Yip
Under review
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By encoding raw observations of obstacle geometry into a latent representation, we can distill the capabilities of an expensive planning algorithm into a neural network, allowing significant speedups in finding near-optimal collision-free paths.

Motion Planning Networks
Ahmed Qureshi, Anthony Simeonov, Mayur J. Bency, Michael C. Yip
ICRA, 2019
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This paper is subsumed by our journal paper.

Bundled super-coiled polymer artificial muscles: Design, characterization, and modeling
Anthony Simeonov, Taylor Henderson, Zixuan Lan, Guhan Sundar, Adam Factor, Jun Zhang, Michael C. Yip
RA-L, 2018

Empirical study of the performance tradeoffs between different bundling techniques for thermally-driven, compliant artificial muscle actuators.

Stickman: Towards a Human Scale Acrobatic Robot
Morgan T. Pope, Steven Christensen, David Christensen, Anthony Simeonov, Grant Imahara, G√ľnter Niemeyer
ICRA, 2018

We present a two DOF robot that is launched from a gravity-driven pendulum and executes a variety of mid-air stunts. The robot uses a combination of onboard sensors to perform state estimation and inform actuator timing.

This research is part of the basis of Imagineering's Stuntronics project.

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles
Jun Zhang, Anthony Simeonov, Michael C. Yip
Smart Materials and Structures, 2018

A new modeling scheme for capturing the coupled hysteresis between tension, strain, and input, which is used for improved control of artificial muscle actuators.

Modeling and inverse compensation of hysteresis in supercoiled polymer artificial muscles
Jun Zhang, Kaushik Iyer, Anthony Simeonov, Michael C. Yip
RA-L, 2017

We propose three new models for characterizing the hysteresis present in super-coiled polymer artificial muscles, and demonstrate their use for accurate open-loop control.