Learning When to Stop: Efficient Active Tactile Perception with Deep Reinforcement Learning

Actively guiding attention is an important mechanism to employ limited processing resources efficiently. The Recurrent Visual Attention Model (RAM) has been successfully applied to process large input images by sequentially attending to smaller image regions with an RL framework. In tactile perception, sequential attention methods are required naturally due to the limited size of the tactile receptive field. The concept of RAM was transferred to the haptic domain by the Haptic Attention Model (HAM) to iteratively generate a fixed number of informative haptic glances for tactile object classification. We extend HAM to a system capable of actively determining when sufficient haptic data is available for reliable classification. To this end, we introduce a hybrid action space, augmenting the continuous glance location with the discrete decision of when to classify. This allows balancing the cost of obtaining new samples against the cost of misclassification, resulting in an optimized number of glances while maintaining reasonable accuracy. We evaluate the efficiency of our approach on a handcrafted dataset, which allows us to compute the most efficient glance locations.