AxisGuide: Grounding Robot Action Coordinate System in RGB Observations for Robust Visuomotor Manipulation
arXiv:2606.06761v1 Announce Type: cross 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 coordinat
The paper addresses a core limitation in current visuomotor manipulation policies, which are gaining significant traction but face practical hurdles in robustness and generalization, pushing for immediate solutions to these issues.
This research provides a fundamental advancement in robot action understanding, crucial for more reliable and adaptable robotic systems, directly impacting the deployment of advanced automation in diverse real-world environments.
Robot manipulation policies will become more robust and generalizable to unseen object locations and environmental shifts, moving beyond brittle behavior cloning towards more intelligent and adaptive action execution.
- · Robotics companies
- · Automation sector
- · AI hardware manufacturers
- · Logistics and manufacturing industries
- · Companies with highly specialized, rigid robotic systems
- · Manual labor in repetitive manipulation tasks
Robots will be able to perform delicate and complex manipulation tasks with greater accuracy and less supervision in dynamic environments.
This improved capability will accelerate the adoption of robots in new sectors, reducing operational costs and increasing efficiency.
The enhanced Dexterity could lead to new applications for robots in challenging domains like disaster recovery or personalized manufacturing, previously constrained by their limited adaptability.
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Read at arXiv cs.AI