Reproduce “Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions” on OpenResearch
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About this paper
André Yuji Yasutomi, Hideyuki Ichiwara, Hiroshi Ito, Hiroki Mori, Tetsuya Ogata
The SAP-RL-E model integrates visual spatial attention with proprioceptive data and offline deep reinforcement learning to automate the peg-in-hole task, achieving a 97.4% average success rate and 7.65-second completion time on concrete under varying lighting conditions. This approach enables robots to reliably insert anchor bolts in construction environments despite challenging visual and contact dynamics.
Key insights
- End-to-end joint training of visual spatial attention and a DRL policy enables the system to learn highly robust, task-specific features critical for successful manipulation under variable conditions.
- Offline training using a real-world 'hole map' from a single hole provides a practical and efficient solution for addressing the reality gap and reducing training time.
- Spatial attention networks can be effectively designed to maintain focus on relevant task regions, even when faced with significant visual occlusions or misleading shadows.
Summary by alphaXiv.
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