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.

翻译：主动感知是人类应对固有部分可观测环境中不确定性的基本能力。对于触觉等信息稀疏且局部的感知方式，主动感知变得尤为关键。近年来，主动感知已成为机器人学的重要研究领域。然而，现有方法通常局限于特定任务或依赖强假设，限制了其普适性。为填补这一空白，本研究提出APPLE（主动感知策略学习）框架——一种利用强化学习解决各类主动感知问题的新方法。APPLE通过统一优化目标联合训练基于Transformer的感知模块与决策策略，学习如何主动收集信息。该框架在设计上不局限于特定任务，原则上可应用于广泛的主动感知问题。我们在不同任务中评估了APPLE的两种变体，包括触觉MNIST基准测试中的触觉探索问题。实验证明APPLE具有显著效能，在回归与分类任务中均实现高精度。这些发现彰显了APPLE作为推进机器人主动感知研究的通用框架潜力。