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Kim Listmann

2 accepted papers

2019

Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

IROS 2019poster

Applying Deep Learning to control has a lot of potential for enabling the intelligent design of robot control laws. Unfortunately common deep learning approaches to control, such as deep reinforcement learning, require an unrealistic amount of interaction with the real system, do not yield any perfo…

Cited by 92SourceScholar
2019

HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints

CoRL 2019

Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an o

Cited by 0SourcePDFScholar