'Touch dreaming' helps humanoid robots handle five tricky tasks with 90.9% higher success

https://arxiv.org/abs/2604.13015 https://humanoid-touch-dream.github.io/ Researchers at Carnegie Mellon University (CMU) and the Bosch Center for AI recently developed a new artificial intelligence (AI)-based system that could improve the ability of humanoid robots to perform dexterous whole-body manipulation in contact-rich real-world settings. Their proposed AI model, dubbed Humanoid Transformer with Touch Dreaming (HTD), was introduced in a paper published on the arXiv pre-print server. "Across five real-world tasks, namely insert-T, book organization, towel folding, cat litter scooping, and tea serving, HTD achieved a 90.9% relative improvement in average success rate over the stronger ACT baseline. "Our ablations also showed that simply adding touch as an extra input is not enough. Predicting tactile signals in latent space was more effective than predicting raw tactile signals directly, yielding a 30% relative gain in success rate over raw tactile dreaming."