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Title: SCAPE: Learning Stiffness Control from Augmented Position Control Experiences
Award ID(s):
1925082 1749204 1724157 1638107
NSF-PAR ID:
10301966
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Conference on Robot Learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-output linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal model based CBF-CLF-QP, resulting in the Reinforcement Learning based CBF-CLF-QP (RL-CBF-CLF-QP), which addresses the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty. 
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