用强化学习让机器人主动感知，通用性强。
Apple: Toward General Active Perception via Reinforcement Learning
- 用Transformer和强化学习联合训练感知与决策模块。
- 在触觉识别任务上准确率达95%以上。
- 适合需要主动探索的机器人感知场景。
主动感知是人类应对部分可观测环境不确定性的重要能力，尤其在触觉等信息稀疏的感官中尤为关键。近年来，主动感知成为机器人领域的重要研究方向，但现有方法多局限于特定任务或依赖强假设，缺乏通用性。为此，本文提出APPLE（Active Perception Policy Learning）框架，利用强化学习解决多种主动感知问题。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，学习如何主动获取信息。该框架不绑定特定任务，理论上可适用于广泛的主动感知问题。我们在多个任务上评估APPLE的两个变体，包括来自Tactile MNIST基准的触觉探索任务。实验表明，APPLE在回归与分类任务中均取得高精度，验证了其作为通用主动感知框架的潜力。
原文摘要 · Abstract (English)
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
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