Publication
Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach
Tim Schneider; Cristiana de Farias; Roberto Calandra; Liming Chen; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2505.06182, Pages 1-20, arXiv, 2025.
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. 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.