Back to Search Start Over

Curriculum-based Sensing Reduction in Simulation to Real-World Transfer for In-hand Manipulation

Authors :
Tao, Lingfeng
Zhang, Jiucai
Zheng, Qiaojie
Zhang, Xiaoli
Tao, Lingfeng
Zhang, Jiucai
Zheng, Qiaojie
Zhang, Xiaoli
Publication Year :
2023

Abstract

Simulation to Real-World Transfer allows affordable and fast training of learning-based robots for manipulation tasks using Deep Reinforcement Learning methods. Currently, Sim2Real uses Asymmetric Actor-Critic approaches to reduce the rich idealized features in simulation to the accessible ones in the real world. However, the feature reduction from the simulation to the real world is conducted through an empirically defined one-step curtail. Small feature reduction does not sufficiently remove the actor's features, which may still cause difficulty setting up the physical system, while large feature reduction may cause difficulty and inefficiency in training. To address this issue, we proposed Curriculum-based Sensing Reduction to enable the actor to start with the same rich feature space as the critic and then get rid of the hard-to-extract features step-by-step for higher training performance and better adaptation for real-world feature space. The reduced features are replaced with random signals from a Deep Random Generator to remove the dependency between the output and the removed features and avoid creating new dependencies. The methods are evaluated on the Allegro robot hand in a real-world in-hand manipulation task. The results show that our methods have faster training and higher task performance than baselines and can solve real-world tasks when selected tactile features are reduced.

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1438479409
Document Type :
Electronic Resource