Title: Towards Contact Robust Locomotion: Planning and Estimation Methods for Robot Locomotion under Uncertainty in Contact Conditions

Date: Friday, October 29, 2021

Time: 3:00 – 5:00pm EST

Location (Virtual): https://bluejeans.com/746230351/4897

Location (In person): Manufacturing Related Disciplines Complex (MRDC) Room 2407

Luke Drnach

Robotics PhD Student

School of Electrical and Computer Engineering

Georgia Institute of Technology

Committee:

Dr. Ye Zhao (Advisor) – George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology

Dr. Frank Dellaert – School of Interactive Computing, Georgia Institute of Technology

Dr. Seth Hutchinson – School of Interactive Computing, Georgia Institute of Technology

Dr. Jun Ueda – George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology

Dr. Patricio Vela – School of Electrical and Computer Engineering, Georgia Institute of Technology

Abstract:

As robots move from the laboratory into real world environments, accounting for model uncertainty will become increasingly important. Current optimization-based motion planning techniques can produce dynamic robot motions for a variety of tasks; however, these method require a precise environment model, which can be intractable to generate for all real-world scenarios. To account for uncertainty in contact modeling during motion planning, we have investigated two separate robust planning techniques. First, we converted the uncertain contact constraints into a robust cost, and demonstrated that the resulting optimization reduces sliding time under frictional uncertainty  and increases foot clearance under contact distance uncertainty. Second, we explored converting the stochastic constraints into deterministic chance constraints, and we combined them with our robust cost. We showed that the chance constraints alone do not add robustness, but can mediate the tradeoff between following the robust and nominal trajectories at constant contact uncertainty.

Inspired by our work on contact-robust optimization, our proposed work focuses on developing an optimization-based approach to update contact model uncertainty from executed trajectory data. We will develop our approach by inverting the complementarity model of contact; we will estimate the errors in the contact model and estimate the forces given the executed trajectory, and combine the errors with the prior contact model using Gaussian process regression. Our approach will be evaluated first in a simple sliding block example and then in a robotic quadruped example.