2026-09-19T09:04:03Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1519252025-12-03T15:12:25Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Kim, Sangbae
Schwendeman, Laura A.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2023-08-23T16:19:31Z
2023-08-23T16:19:31Z
2023-06
2023-07-18T16:17:28.958Z
https://hdl.handle.net/1721.1/151925
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.
S.B.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
Improved Friction and Dynamics Estimation in Legged
Robots
Thesis
application/pdf
Bachelor
Bachelor of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>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.</Abstract>
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