arXiv:2606.05533v1 Announce Type: new Abstract: Existing robot planning systems rely on appearance-based reasoning, where visual observations are encoded into latent spaces organized around object appearances (e.g., recognizing a "cart" based on how it looks). However, planning requires reasoning about task-relevant functionalities of objects (e.g., whether an object is "movable"), which appearance-based latent spaces do not capture. As a result, existing approaches struggle to generalize to novel robot-object interactions. We address this limited generalizability through affordance reasoning,
The increasing sophistication of robotic systems and the desire for greater autonomy demand more effective and generalizable object interaction capabilities.
This research addresses a fundamental limitation in robot planning, moving beyond appearance-based recognition to task-relevant functional reasoning, crucial for practical robotic deployment.
Robot planning systems will begin to integrate functional latent spaces, allowing robots to understand objects by their potential uses ('affordances') rather than just their visual properties.
- · Robotics industry
- · AI hardware manufacturers
- · Logistics and manufacturing sectors
- · AI researchers
- · Companies relying solely on appearance-based object recognition
Robots will become significantly more adaptable and versatile in unstructured environments.
This improved adaptability will accelerate the deployment of robots into novel and complex real-world applications beyond factories.
Generalized robot capabilities, driven by affordance reasoning, could underpin the development of more sophisticated autonomous agents for various industries, potentially impacting labor markets and operational efficiencies.
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Read at arXiv cs.LG