Differential Amplifier-Inspired AmpAttention for Multi-View Robotic Manipulation
This paper proposes AmpAttention, a novel attention mechanism inspired by differential amplifiers in analog circuits, to suppress attention drift in multi-view robotic manipulation. The RVAF model, built on AmpAttention, achieves state-of-the-art average success rates on 18 RLBench tasks (249 variations) while reducing training time by 33.3%. Its extension RVAF++, incorporating the SAM2 image encoder, reaches a 91% success rate on the 'insert peg' task. The work has been accepted at IROS2026.
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[Submitted on 3 Jul 2026]
Title:Differential Amplifier-Inspired AmpAttention for Multi-View Robotic Manipulation
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Abstract:Multi-view robotic manipulation methods with the attention mechanism have recently achieved significant progress in both training efficiency and task performance. However, the inherent redundancy, occlusion, and viewpoint dependency in robotic view images often lead to severe attention drift. To address this challenge, we propose AmpAttention, a novel attention mechanism inspired by differential amplifiers in analog circuits. It aims to suppress attention noise and capture high signal-to-noise ratio signals for more reliable perception. Based on this, we introduce the RVAF model, which integrates task-guided intra-view and inter-view AmpAttention. Compared to previous state-of-the-art methods, RVAF achieves the optimal average success rate across 18 RLBench tasks (249 variations) while reducing training time by 33.3\%. RVAF also demonstrates strong potential in real-world high-precision tasks, exemplified by its ability to pick up a dart and accurately insert it into the red bullseye. Furthermore, we extend RVAF to RVAF++ by incorporating the SAM2 image encoder. RVAF++ achieves substantial gains on high-precision tasks, achieving a 91\% success rate on the `insert peg' task. More qualitative results are provided at the anonymous project website this https URL.
Comments: Accepted by IROS2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.02845 [cs.RO]
(or arXiv:2607.02845v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.02845
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jin Yang [view email] [v1] Fri, 3 Jul 2026 00:48:44 UTC (854 KB)
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