Improved Friction and Dynamics Estimation in Legged Robots

Reducing the Sim-to-Real gap between robot simulation and robot performance could lead to improved and more efficient robot design through more accurate controls and design testing in simulation and through more accurate state detection for model-based control architectures. This work built upon current research in the field of robot system dynamics by investigating the effect of using single-layer feed-forward neural nets to model non-linear friction forces and other forms of dynamics that are difficult to account for with traditional robot system identification schemes. Applying the single-layer feed-forward neural nets to system identification data from the dynamic MIT Humanoid and MIT Mini Cheetah robots significantly reduced torque prediction errors. The neural net was able to reduce torque errors by modeling both linear and non-linear effects that could not be easily fit by traditional methods. The results of this paper suggest that using the system identification methodology outlined within could lead to more accurate dynamics modeling, which would assist with closing the Sim-to-Real gap through simulated dynamics with more fidelity and a more robust representation of robot dynamics.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Schwendeman, Laura A.

Advisor dc:contributor.advisor

• Kim, Sangbae

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

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

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151925

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151925