arXiv:2607.07101v1 Announce Type: cross Abstract: Proprioception is fundamental to robotic manipulation, yet standard fusion methods often treat it as an isolated vector lacking explicit alignment with visual tokens. Without a direct correspondence between 3D kinematics and 2D feature maps, manipulation policies struggle to ground the robot's state within the scene, frequently underperforming even vision-only baselines. To address this, we introduce GeoProp, a lightweight, plug-and-play adapter that aligns proprioception with vision through explicit geometric grounding and spatial feature samp
The rapid advancements in large language models and generalist policies for robotics necessitate better integration of proprioception and vision to overcome current limitations in robot manipulation tasks.
Improved robot manipulation, enabled by better sensory integration, is critical for accelerating the deployment of autonomous systems in diverse real-world applications, including manufacturing, logistics, and eventually, generalist humanoid robots.
Policies built on models like GeoProp will be more robust and capable of complex, unscripted manipulation by tightly integrating robot state with visual scene understanding, bridging a key gap in current robotic systems.
- · Robotics companies
- · Automation industries
- · AI hardware manufacturers
- · Logistics and manufacturing sectors
- · Tasks requiring highly manual dexterity
- · Current vision-only robot manipulation approaches
- · Robotics platforms lacking robust sensory integration
Robots will perform manipulation tasks with significantly higher precision and adaptability.
This advancement could accelerate the development and commercial viability of general-purpose humanoid robots.
More capable robots could lead to widespread automation, impacting labor markets and supply chain dynamics globally.
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Read at arXiv cs.AI