Reinforcement Learning for Pivoting Task

By Rika Antonova, Silvia Cruciani, Christian Smith, Danica Kragic. 2017

73 citations recorded by the source corpus.

In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies withou ...

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Control and Systems Engineering

In Reinforcement Learning

Citations in the corpus

Domain randomization for transferring deep neural networks from simulation to the real world (2017, background): “Similarly, in [2], the authors use a simulator with randomized friction and action delay and find behaviors transfer to the real world.”
Solving Rubik's Cube with a Robot Hand (2019, background): “This idea has also been extended to dynamics randomization [80, 3, 105, 119, 77] to learn a robust policy that transfers to similar environments but with different dynamics.”
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience (2018, background): “ination of system identiﬁcation and dynamics randomization has been used in the past to learn locomotion for a real quadruped [26], non-prehensile object manipulation [27] and in-hand object pivoting [28]. In our work, we recognize domain randomization and system identiﬁcation as powerful tools for ”
Learning dexterous in-hand manipulation (2018, background): “This idea has also been extended to dynamics randomization [4, 61, 68] to learn a robust policy that transfers to similar environments but with different dynamics.”
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization (2017, background): “[25], where randomized dynamics was used to transfer manipulation policies from simulation to the real world.”
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks (2018, background): “There have been more robotics works that do not use vision, but that apply domain randomization on physical properties of the simulator to aid transferability [40, 46, 1, 68, 43].”
Trends and challenges in robot manipulation (2019, background): “They are also capable of performing a variety of simple manipulation actions such as throwing ( 58 ), sliding ( 59 ), poking ( 60 ), pivoting ( 61 ), and pushing ( 62 ).”
Real-world humanoid locomotion with reinforcement learning (2023, methodology): “Our approach falls in the general family of techniques for sim-to-real transfer with domain randomization ( 28–31 ).”
VPE: Variational Policy Embedding for Transfer Reinforcement Learning (2018, background): “Domain randomization attempts to find a single policy that works for all instances of a family [7], [8].”
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design (2020, background): “This is true for a diverse array of methods including transfer learning (e.g., [2, 48, 32, 41]), robust RL (e.g., [4, 16, 28]), unsupervised RL (e.g., [15]), and emergent complexity (e.g., [38, 45, 46]).”
Robust Visual Domain Randomization for Reinforcement Learning. (2019, background): “Domain randomization, in which the visual and physical properties of the training domains are randomized at the start of each episode during training, has been shown to lead to improved generalization and transfer to the real world with little or no real world data (Mordatch et al., 2015; Rajeswaran”