Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract)

Authors

  • Hanxiao Chen Harbin Institute of Technology

Keywords:

Robotic Manipulation, Reinforcement Learning, State Representation Learning, Imitation Learning

Abstract

Humans possess the advanced ability to grab, hold, and manipulate objects with dexterous hands. What about robots? Can they interact with the surrounding world intelligently to achieve certain goals (e.g., grasping, object-relocation)? Actually, robotic manipulation is central to achieving the premise of robotics and represents immense potential to be widely applied in various scenarios like industries, hospitals, and homes. In this work, we aim to address multiple robotic manipulation tasks like grasping, button-pushing, and door-opening with reinforcement learning (RL), state representation learning (SRL), and imitation learning. For diverse missions, we self-built the PyBullet or MuJoCo simulated environments and independently explored three different learning-style methods to successfully solve such tasks: (1) Normal reinforcement learning methods; (2) Combined state representation learning (SRL) and RL approaches; (3) Imitation learning bootstrapped RL algorithms.

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Published

2021-05-18

How to Cite

Chen, H. (2021). Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15769-15770. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17881

Issue

Section

AAAI Student Abstract and Poster Program