[2606.06761] AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation
[Submitted on 4 Jun 2026]
Title:AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation
View a PDF of the paper titled AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation, by Jiyun Jang and 7 other authors
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Abstract:Visuomotor manipulation policies trained via large-scale behavior cloning have achieved strong semantic scene understanding, yet often fail to reliably execute correct low-level actions under distribution shifts. For example, even in a simple pickup task with identical scene layouts, camera viewpoints, and illumination, performance can degrade substantially when the object is placed at unseen locations. We argue that this gap arises from insufficient action understanding, namely the inability to interpret the robot's base-frame action coordinate system in image space. To address this issue, we introduce AxisGuide, a lightweight guidance method that bridges semantic scene understanding and action-coordinate interpretation. Using camera parameters and end-effector poses, AxisGuide renders the robot base-frame axes in each camera view and augments RGB observations with a small set of cue channels that explicitly visualize the meaning of the +x, +y, and +z motions in image space. Extensive evaluations in both the LIBERO simulation and real-world environments demonstrate that AxisGuide yields substantial performance gains and improved generalization, highlighting the effectiveness of explicit action-coordinate cues for learning reliable and transferable generalist visuomotor policies.
Comments: Accepted to Robotics: Science and Systems (RSS) 2026
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.06761 [cs.RO]
(or arXiv:2606.06761v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.06761
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jiyun Jang [view email] [v1] Thu, 4 Jun 2026 22:57:34 UTC (13,390 KB)
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