Learning Robust Terrain-Aware Locomotion

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Margolis, Gabriel B.

Advisor dc:contributor.advisor

• Agrawal, Pulkit

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139325

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139325