ActiveScale: Scaling Active Perception for Robots across Model, Data, and Hardware

2026-09-16

AI Summary

为解决机器人操作中视角变化带来的信息遮挡问题，ActiveScale通过模型、数据和硬件协同设计，增强视觉-语言-动作模型的理解能力，提高主动感知任务成功率。

Abstract

Active perception is essential for robotic manipulation when fixed viewpoints leave task-relevant information occluded or unobserved. However, enabling vision-language-action (VLA) models to reason across changing viewpoints and actively acquire informative observations remains challenging. We present ActiveScale, a framework that advances active perception through coordinated model, data, and hardware designs. Our model augments a VLA with historical video observations and explicit camera-pose supervision, using per-frame pose tokens and a lightweight prediction head to associate observations across viewpoints and support a coherent understanding of the scene. To learn from the camera motion naturally present in human activity, we introduce a scalable human--robot mid-training recipe using 1000 hours of egocentric and robotic data, adapting the model to temporal inputs and pose supervision. We further introduce Active-perception Mobile-manipulation Platform (AMP), a robotic platform that supports active perception and mobile manipulation through single-operator teleoperation, enabling scalable collection of demonstrations that coordinate viewpoint changes and manipulation. Experiments demonstrate improved success rates on active-perception tasks, while ablation studies validate the contributions of camera-pose-aware modeling and egocentric mid-training. Together, these components provide an integrated foundation for studying and developing active perception in robotic manipulation.

Problem

• Active Perception
• Robotic Manipulation
• Vision-Language-Action Models
• Viewpoint Changes
• Informative Observations

Innovation

• Active Perception
• Vision-Language-Action Models
• Camera-Pose Supervision
• Egocentric Mid-Training
• Mobile Manipulation Platform