Action Conditioned Tactile Prediction: case study on slip prediction

TL;DR

This paper introduces two novel action-conditioned tactile prediction models tested on a challenging magnetic tactile sensor, demonstrating superior performance over existing models in real-world robotic manipulation tasks.

Contribution

The paper presents two new data-driven tactile prediction models and provides the first comprehensive comparison with existing models using a large real-world dataset.

Findings

Proposed models outperform existing models in qualitative assessments.

Models achieve higher slip prediction accuracy.

Large dataset enables robust evaluation of tactile prediction methods.

Abstract

Tactile predictive models can be useful across several robotic manipulation tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand manipulation. However, available tactile prediction models are mostly studied for image-based tactile sensors and there is no comparison study indicating the best performing models. In this paper, we presented two novel data-driven action-conditioned models for predicting tactile signals during real-world physical robot interaction tasks (1) action condition tactile prediction and (2) action conditioned tactile-video prediction models. We use a magnetic-based tactile sensor that is challenging to analyse and test state-of-the-art predictive models and the only existing bespoke tactile prediction model. We compare the performance of these models with those of our proposed models. We perform the comparison study using our novel tactile-enabled dataset containing 51,000 tactile frames of a real-world robotic manipulation task with 11 flat-surfaced household objects. Our experimental results demonstrate the superiority of our proposed tactile prediction models in terms of qualitative, quantitative and slip prediction scores.