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The paper introduces Tabero, a benchmark and model suite for gentle, language-conditioned robotic manipulation using vision, touch, and language. To overcome the scarcity of aligned vision-tactile-language data, they present a data-efficient pipeline that repurposes open-source robot manipulation trajectories. They propose Tabero-VTLA, an architecture with a decoupled force-position command interface executed by a hybrid controller, achieving high task success while significantly reducing grip force.
Robots can now learn to "handle with care," reducing grip force by over 70% in gentle manipulation tasks, thanks to a new vision-tactile-language model and benchmark.
Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for gentle manipulation due to scarce aligned vision-tactile-language data and the lack of effective closed-loop force feedback mechanisms. To address these challenges, we introduce Tabero, a benchmark and model suite for gentle, language-conditioned robotic manipulation that demands fine-grained contact force perception. First, the Tabero benchmark addresses the scarcity of tactile data by presenting a data-efficient pipeline that repurposes open-source robot manipulation trajectories to generate diverse vision-tactile-language tasks, and establishes a multidimensional evaluation protocol that measures task success alongside physical interaction quality. Second, we propose Tabero-VTLA, an architecture with a decoupled force-position command interface; the resulting force-position commands are executed by a fixed hybrid controller to enable real-time, force-aware manipulation. Evaluated on Tabero, our model maintains high task success while reducing average grip force by over 70\% under gentle instructions, demonstrating its ability to modulate interaction forces based on multimodal experience. Our code is publicly available at https://github.com/NathanWu7/Tabero.
