Novel Robots

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Exciting, dynamic, and challenging

New robots open up new opportunities we never thought possible. The control design is intertwined with the nature of the system (rather than separate). My entry into this field was the Ionocraft.

Fun learning problem

They also are aligned very closely to my machine learning interests because of their high cost-per-test and unknown dynamics. When creating a new controller it becomes a minimum sample complexity problem — we want the robot to work as soon as possible, rather than being worried about peak performance after hours of training.

Too much uncertainty!

New types of robots suffer from uncertainty in manufacturing, assembly, and everywhere. The methods for using these robots must at least be able to handle these types of uncertainty, but ideally it will be able model and capture them individually.

[1],"A Jumping Silicon Microrobot with Electrostatic Inchworm Motors andEnergy Storing Substrate Springs," 2019.[2]Toward controlled flight of the ionocraft: aflying microrobot using electrohydrodynamic thrust with onboard sensing and nomoving parts." 2018. [3]"First steps of a millimeter-scale walking silicon robot." 2017. [4] "Towards Aerodynamic Control of Miniature Rockets with MEMS ControlSurfaces." 2020 [5]  "An untethered isoperimetric softrobot." 2020. [6]  "Precision jumping limits fromflight-phase control in salto-1p." 2018.[7] Piccolissimo: The smallest micro aerialvehicle." 2017.[8] "Improvised Robotic Design with FoundObjects."

Open areas of investigation:

We explored how MBRL can learn multi-step, nonlinear controllers!
A collections of steps towards a data-driven autonomous microrobot.
A collection of steps towards controlled flight of The Ionocraft, a completely silent microrobot with ion thrust!