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arXiv · 2604.08418

Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction

Marco Gabriele Fedozzi·Yukie Nagai·Francesco Rea·Alessandra Sciutti
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Abstract

Inspired by the human ability to understand and predict others, we study the applicability of Conditional Neural Processes (CNP) to the task of self-supervised multimodal action prediction in robotics. Following recent results regarding the ontogeny of the Mirror Neuron System (MNS), we focus on the preliminary objective of self-actions prediction. We find a good MNS-inspired model in the existing Deep Modality Blending Network (DMBN), able to reconstruct the visuo-motor sensory signal during a partially observed action sequence by leveraging the probabilistic generation of CNP. After a qualitative and quantitative evaluation, we highlight its difficulties in generalizing to unseen action sequences, and identify the cause in its inner representation of time. Therefore, we propose a revised version, termed DMBN-Positional Time Encoding (DMBN-PTE), that facilitates learning a more robust representation of temporal information, and provide preliminary results of its effectiveness in expanding the applicability of the architecture. DMBN-PTE figures as a first step in the development of robotic systems that autonomously learn to forecast actions on longer time scales refining their predictions with incoming observations.

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Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea, Alessandra Sciutti. 2026-04-09. Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction. https://arxiv.org/abs/2604.08418

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