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This paper introduces multi-horizon multimodal prediction in ABBR, an agile tactile World Action Model for contact-rich robot control, which provides supervision for visual latent at a larger temporal offset while predicting the tactile latent in the next frame to capture fine-grained contact dynamics.
Reward models, despite excelling at general response quality, stumble when it comes to capturing individual user preferences, achieving only 76% accuracy on a new personalized benchmark.
