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.
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arxiv.org
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by Hai Nguyen,T... ב- arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18002.pdfשאלות מעמיקות