Learning an Action-Conditional Model for Haptic Texture Generation
This addresses the problem of providing immersive haptic feedback in VR and teleoperation systems, offering a unified model that improves over existing incremental approaches.
The paper tackles the challenge of modeling haptic feedback for virtual reality and teleoperation by developing a learned action-conditional model that predicts induced acceleration from user interactions and material data, showing it outperforms previous methods and generalizes to new actions and material instances.
Rich haptic sensory feedback in response to user interactions is desirable for an effective, immersive virtual reality or teleoperation system. However, this feedback depends on material properties and user interactions in a complex, non-linear manner. Therefore, it is challenging to model the mapping from material and user interactions to haptic feedback in a way that generalizes over many variations of the user's input. Current methodologies are typically conditioned on user interactions, but require a separate model for each material. In this paper, we present a learned action-conditional model that uses data from a vision-based tactile sensor (GelSight) and user's action as input. This model predicts an induced acceleration that could be used to provide haptic vibration feedback to a user. We trained our proposed model on a publicly available dataset (Penn Haptic Texture Toolkit) that we augmented with GelSight measurements of the different materials. We show that a unified model over all materials outperforms previous methods and generalizes to new actions and new instances of the material categories in the dataset.