# Act, Sense, Act: Learning Active Perception from Large-Scale Egocentric Human Data

arXiv: https://arxiv.org/abs/2602.04600 · HTML: https://mlsanity.com/paper/2602.04600/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2026.

## Built on by (most cited first, 3 of 3)

- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [Cognitive Action Reasoning for Proactive Robots from Human-Centered Multimodal Observations](https://mlsanity.com/paper/2609.23486/) (2026)
- [RoboQuest: Generalist Physical Agents that Search, Inspect and Test](https://mlsanity.com/paper/2610.10388/) (2026)

## Abstract (arXiv)

Achieving generalizable manipulation in unconstrained environments requires the robot to proactively resolve information uncertainty, i.e., the capability of active perception. However, existing methods are often confined in limited types of sensing behaviors, restricting their applicability to complex environments. In this work, we formalize active perception as a history-dependent perception-action loop driven by information-seeking action and decision branching, providing a structured categorization of visual active perception paradigms. Building on this perspective, we introduce CoMe-VLA, a cognitive and memory-aware vision-language-action (VLA) framework that leverages large-scale human egocentric data to learn versatile exploration and manipulation priors. Our framework integrates a cognitive auxiliary head for autonomous sub-task transitions and a dual-track memory system to maintain consistent self and environmental awareness by fusing proprioceptive and visual temporal contexts. By aligning human and robot hand-eye coordination behaviors in a unified egocentric action space, we train the model progressively in three stages. Extensive experiments on a wheel-based humanoid have demonstrated strong robustness and adaptability of our proposed method across diverse long-horizon tasks spanning multiple active perception scenarios.