Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation

27 July 2020

Papers citing "Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation"

8 / 8 papers shown

Title
Precision-Focused Reinforcement Learning Model for Robotic Object Pushing Lara Bergmann David P. Leins R. Haschke Klaus Neumann 42 3 0 13 Nov 2024
Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators Fabian Baumeister Lukas Mack Joerg Stueckler 44 2 0 20 Sep 2024
Non-Prehensile Aerial Manipulation using Model-Based Deep Reinforcement Learning Cora A. Dimmig Marin Kobilarov 31 1 0 01 Jul 2024
Biased-MPPI: Informing Sampling-Based Model Predictive Control by Fusing Ancillary Controllers Elia Trevisan Javier Alonso-Mora 27 15 0 17 Jan 2024
Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing Max Yang Yijiong Lin Alex Church John Lloyd Dandan Zhang David A.W. Barton Nathan Lepora OffRL 38 12 0 26 Jul 2023
Sampling-based Model Predictive Control Leveraging Parallelizable Physics Simulations Corrado Pezzato C. Salmi Max Spahn Elia Trevisan Javier Alonso-Mora C. H. Corbato 26 5 0 18 Jul 2023
A Survey of Meta-Reinforcement Learning Jacob Beck Risto Vuorio E. Liu Zheng Xiong L. Zintgraf Chelsea Finn Shimon Whiteson OOD OffRL 42 124 0 19 Jan 2023
Maximizing the Use of Environmental Constraints: A Pushing-Based Hybrid Position/Force Assembly Skill for Contact-Rich Tasks Yunlei Shi Zhaopeng Chen Lin Cong Yansong Wu Martin Craiu-Müller C. Yuan Chunyang Chang Lei Zhang Jianwei Zhang 22 0 0 12 Aug 2022