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Improving Input-Output Linearizing Controllers for Bipedal Robots via Reinforcement Learning.

, , , , , , and . L4DC, volume 120 of Proceedings of Machine Learning Research, page 990-999. PMLR, (2020)

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Probabilistic Safe Online Learning with Control Barrier Functions., , , , , and . CoRR, (2022)Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems., , , , , and . CoRR, (2023)Learning Min-norm Stabilizing Control Laws for Systems with Unknown Dynamics., , , , and . CDC, page 737-744. IEEE, (2020)Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty., , , , and . CDC, page 6762-6769. IEEE, (2021)Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions., , , and . Robotics: Science and Systems, (2020)Combining Model-Based Design and Model-Free Policy Optimization to Learn Safe, Stabilizing Controllers., , , , and . ADHS, volume 54 of IFAC-PapersOnLine, page 19-24. Elsevier, (2021)Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning., , , , and . CoRL, volume 205 of Proceedings of Machine Learning Research, page 2125-2135. PMLR, (2022)Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics., , , , and . ACC, page 3683-3690. IEEE, (2021)In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States., , , , and . L4DC, volume 211 of Proceedings of Machine Learning Research, page 286-299. PMLR, (2023)Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects., , , , and . CoRR, (2020)