Learning Robust Terrain-Aware Locomotion
Name
Margolis-gmargo-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
Size
22.45 MB
Format
Adobe PDF
Checksum (MD5)
a6eee8e287d9815525de2e2c40e65393
Author(s)
Margolis, Gabriel B.
Advisor(s)
Agrawal, Pulkit
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Today’s robotic quadruped systems can walk over a diverse set of natural and complex terrains. Approaches to locomotion based on model-based feedback control are robust to perturbations but cannot easily incorporate visual terrain information. Meanwhile, approaches to locomotion based on learning excel at associating visual sensory data with suitable control policies but often fail to generalize across the gap between simulation and deployment settings. This thesis proposes a trajectory-based abstraction for locomotion through which model-free and model-based control layers interface. This approach enables general visually guided locomotion while preserving robustness. We demonstrate that our proposed architecture allows the Mini Cheetah quadruped to match theoretical performance limits in a set of visual tasks. The robustness and practicality afforded by our approach are demonstrated through evaluation on hardware.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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