APPLE: Toward General Active Perception via Reinforcement Learning

Tim Schneider, Cristiana de Farias, Roberto Calandra, Liming Chen, Jan Peters

Keywords: Active Perception; Reinforcement Learning; Tactile Sensing

TL;DR: APPLE is a reinforcement learning framework for general active perception which we evaluate on a range of different tasks, focussed on tactile perception

Abstract:

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.
Hence, in recent years, it 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.
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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Submission Number: 36