Abstract: Many planning and control approaches in robotics have converged on optimization-based formulations, with recent advances achieved by leveraging significant data and compute to attempt to tackle these nonlinear and non-convex problems. In this thesis, we instead focus on local models and demonstrate their benefits and surprising effectiveness. In the case of smooth optimization, the local model is a convex quadratic program. We show how this structure enables efficient parallelization and scaling by mapping it to a neural network, and that a fixed linear model is still capable of basic locomotion tasks even with a large sim-to-real gap. We then look at the non-smooth case that arises in contact-implicit approaches which can be expressed as quadratic programs with complementarity constraints and develop a C++ solver, Marble, that leverages the structure of relaxed complementarity. Finally, we propose future work that unifies existing hard and soft contact models under a common framework and examines them in the context of planning. We also propose applying the resulting Marble solver for local motion retargeting tasks, exploring applications in both simulation and on hardware in an iterative learning control context.

Thesis Committee: 
Zac Manchester (chair)
Aaron Johnson
Lorenz Biegler
Pat Wensing (University of Notre Dame)

Thesis URL