Zero-Shot Transfer of Haptics-Based Object Insertion Policies

Author:

Brahmbhatt Samarth1,Deka Ankur1,Spielberg Andrew2,Müller Matthias1

Affiliation:

1. Intel Labs

2. Massachusetts Institute of Technology

Publisher

IEEE

Reference43 articles.

1. Imitation Learning for High Precision Peg-in-Hole Tasks

2. robosuite: A modular simulation framework and benchmark for robot learning;zhu;ArXiv Preprint,2020

3. A Learning-Based Framework for Robot Peg-Hole-Insertion

4. Proximal policy optimization algorithms;schulman;ArXiv Preprint,2017

5. Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep-Reinforcement-Learning Approach

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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