Deep Black-Box Reinforcement Learning with Movement Primitives

Deep Black-Box Reinforcement Learning with Movement Primitives

18 October 2022

Gerhard Neumann

Papers citing "Deep Black-Box Reinforcement Learning with Movement Primitives"

10 / 10 papers shown

Title
Simulation-Aided Policy Tuning for Black-Box Robot Learning Shiming He Alexander von Rohr Dominik Baumann Ji Xiang Sebastian Trimpe 77 0 0 21 Nov 2024
BMP: Bridging the Gap between B-Spline and Movement Primitives Weiran Liao Ge Li Hongyi Zhou Rudolf Lioutikov Gerhard Neumann 31 0 0 15 Nov 2024
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li Dong Tian Hongyi Zhou Xinkai Jiang Rudolf Lioutikov Gerhard Neumann OffRL 163 2 0 12 Oct 2024
Use the Force, Bot! -- Force-Aware ProDMP with Event-Based Replanning Paul Werner Lodige Maximilian Xiling Li Rudolf Lioutikov 17 1 0 17 Sep 2024
Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning Piotr Kicki Davide Tateo Puze Liu Jonas Guenster Jan Peters Krzysztof Walas 26 2 0 26 Aug 2024
Bayesian Optimization for Sample-Efficient Policy Improvement in Robotic Manipulation Adrian Rofer Iman Nematollahi Tim Welschehold Wolfram Burgard Abhinav Valada 32 0 0 21 Mar 2024
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts Onur Celik Aleksandar Taranovic Gerhard Neumann 26 7 0 11 Mar 2024
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Ge Li Hongyi Zhou Dominik Roth Serge Thilges Fabian Otto Rudolf Lioutikov Gerhard Neumann OffRL 17 7 0 21 Jan 2024
MP3: Movement Primitive-Based (Re-)Planning Policy Fabian Otto Hongyi Zhou Onur Celik Ge Li Rudolf Lioutikov Gerhard Neumann 18 5 0 22 Jun 2023
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives Ge Li Zeqian Jin Michael Volpp Fabian Otto Rudolf Lioutikov Gerhard Neumann Karlsruhe Institute of Technology MU 11 38 0 04 Oct 2022