arXiv:2606.14981v1 Announce Type: cross Abstract: Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution. While prior methods typically perform this verification only with visual observations, vision alone is often insufficient for contact-rich manipulation, where success depends on both global task progress and subtle local interactions such as contact force. We introduce ViTaL, a visuo-tactile inference-time steering framework that formulates multimodal guidance as a bi-level optimization problem. At the high lev
The continuous development of robot manipulation requires more nuanced sensory integration to enable real-world deployment, especially in contact-rich tasks.
This research represents a significant step towards more capable and reliable autonomous robotic systems, which are critical for advancements in automation and complex physical tasks.
Robot policies can now be more effectively steered during inference by combining visual and tactile information, leading to more robust and precise manipulation capabilities.
- · Robotics companies
- · Automation sector
- · AI hardware manufacturers
- · Companies relying solely on visual-only robotic manipulation
- · Manual labor in repetitive tasks
More sophisticated industrial robots capable of handling delicate or complex assembly tasks.
Increased demand for advanced tactile sensors and robust sensor fusion architectures in robotics.
Acceleration of general-purpose robot development, potentially impacting a wider array of industries beyond manufacturing.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI