Основные понятия
The author proposes leveraging domain symmetry through data augmentation and auxiliary losses to enhance a recurrent SAC agent for solving symmetric POMDPs efficiently.
Аннотация
This study focuses on improving robotic assembly tasks using a soft wrist by integrating data augmentation and auxiliary losses in training an actor-critic DRL agent. The proposed method leverages the potential symmetry of the task to achieve sample-efficient learning. Experimental evaluations across five symmetric peg shapes show promising results, with the proposed agent outperforming state-based agents in simulation. The approach allows for direct learning on real robots within a short timeframe, demonstrating the effectiveness of leveraging symmetry in robotic assembly tasks.
Статистика
Results show that our proposed agent can be comparable to or even outperform a state-based agent.
Sample efficiency allows us to learn directly on the real robot within 3 hours.
Demonstrations are used to overcome reward sparsity.
The learned policies are better than human demonstrations.
The learned policy generalizes well across different peg shapes.
Цитаты
"Our method's versatility extends beyond the peg-in-hole task with a soft wrist and can be adapted for other scenarios."
"The proposed agent outperforms state-based agents in simulation, showcasing the effectiveness of leveraging symmetry in robotic assembly tasks."