Active perception is a fundamental skill that enables us humans to deal with uncertainty in our inherently partially observable environment. For senses such as touch, where the information is sparse and local, active perception becomes crucial. In recent years, active perception has emerged as an important research domain in robotics. However, current methods are often bound to specific tasks or make strong assumptions, which limit their generality. To address this gap, this work introduces APPLE (Active Perception Policy Learning) - a novel framework that leverages reinforcement learning (RL) to address a range of different active perception problems. APPLE jointly trains a transformer-based perception module and decision-making policy with a unified optimization objective, learning how to actively gather information. By design, APPLE is not limited to a specific task and can, in principle, be applied to a wide range of active perception problems. We evaluate two variants of APPLE across different tasks, including tactile exploration problems from the Tactile MNIST benchmark. Experiments demonstrate the efficacy of APPLE, achieving high accuracies on both regression and classification tasks. These findings underscore the potential of APPLE as a versatile and general framework for advancing active perception in robotics. Project page: https://timschneider42.github.io/apple

翻译：主动感知是一项基本技能，使我们人类能够在固有的部分可观测环境中应对不确定性。对于触觉等感官，由于信息稀疏且局部化，主动感知变得至关重要。近年来，主动感知已成为机器人学中一个重要的研究领域。然而，当前方法往往局限于特定任务或做出强假设，限制了其通用性。为解决这一问题，本文引入了APPLE（主动感知策略学习）——一种新颖的框架，利用强化学习来处理一系列不同的主动感知问题。APPLE联合训练一个基于Transformer的感知模块和决策策略，采用统一的优化目标，学习如何主动收集信息。通过设计，APPLE不局限于特定任务，原则上可应用于广泛的主动感知问题。我们在不同任务上评估了APPLE的两种变体，包括来自Tactile MNIST基准的触觉探索问题。实验证明了APPLE的有效性，在回归和分类任务上均实现了高精度。这些发现强调了APPLE作为机器人学中推动主动感知发展的多功能通用框架的潜力。项目页面：https://timschneider42.github.io/apple