Cargando…

Learning adaptive reaching and pushing skills using contact information

In this paper, we propose a deep reinforcement learning-based framework that enables adaptive and continuous control of a robot to push unseen objects from random positions to the target position. Our approach takes into account contact information in the design of the reward function, resulting in...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Shuaijun, Sun, Lining, Zha, Fusheng, Guo, Wei, Wang, Pengfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10536165/
https://www.ncbi.nlm.nih.gov/pubmed/37781411
http://dx.doi.org/10.3389/fnbot.2023.1271607
Descripción
Sumario:In this paper, we propose a deep reinforcement learning-based framework that enables adaptive and continuous control of a robot to push unseen objects from random positions to the target position. Our approach takes into account contact information in the design of the reward function, resulting in improved success rates, generalization for unseen objects, and task efficiency compared to policies that do not consider contact information. Through reinforcement learning using only one object in simulation, we obtain a learned policy for manipulating a single object, which demonstrates good generalization when applied to the task of pushing unseen objects. Finally, we validate the effectiveness of our approach in real-world scenarios.