This study introduces a novel approach to robotic assembly tasks using symmetry-aware reinforcement learning. By leveraging domain symmetry, the proposed agent shows promising results in simulation and real-world hardware experiments. The method combines data augmentation and auxiliary losses to enhance learning efficiency and performance.
The content discusses the challenges of contact-rich peg-in-hole tasks in robotic assembly and the limitations of traditional approaches using rigid robots. It highlights the benefits of employing soft robots for such tasks due to their ability to handle low-frequency control signals safely.
By adopting a partially observable formulation and deep reinforcement learning, the study aims to train a memory-based agent purely based on haptic and proprioceptive signals. Leveraging potential domain symmetry allows for sample-efficient learning through data augmentation and auxiliary losses.
Experimental evaluations across various symmetric peg shapes demonstrate that the proposed agent can outperform state-based agents while achieving sample efficiency. The study also explores policy generalization across different peg shapes and successful transfer from simulation to real-world hardware experiments.
Furthermore, comparisons with existing works in pose estimation, soft robot control, and symmetry-aware policy learning provide insights into the novelty and effectiveness of the proposed method. The content concludes with discussions on limitations, future research directions, and implications for improving robotic assembly tasks.
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arxiv.org
ข้อมูลเชิงลึกที่สำคัญจาก
by Hai Nguyen,T... ที่ arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18002.pdfสอบถามเพิ่มเติม