N_0-VTLA: Scaling Vision-Tactile-Language-Action Models with Latent Tactile Tokens

The paper introduces N_0-VTLA, a vision-tactile-language-action foundation model for contact-rich manipulation and offline policy improvement. Its training recipe combines visuo-tactile pretraining on the NeoData robot dataset, staged tactile-pathway integration, and ALTER, an advantage-conditioned offline reinforcement learning method. The authors report wins on all nine NeoReal real-robot tasks and 63.8% mean success across a 20-task simulation suite, compared with 44.0% for the strongest baseline. With ALTER, the policy reaches 75–95% success on three long-horizon real-robot tasks. The results are promising, but the supplied evidence is limited to the paper abstract.