Towards Interpretable Visuo-Tactile Predictive Models for Soft Robot Interactions

TL;DR

This paper develops an interpretable multi-modal perception model for soft robots using generative models to fuse visual and proprioceptive data, enhancing prediction and understanding of contact interactions in complex environments.

Contribution

It introduces a novel generative approach for multi-modal perception in soft robots, improving interpretability of sensory fusion and prediction processes.

Findings

Effective fusion of visual and proprioceptive data achieved

Enhanced interpretability of perception models demonstrated

Implications for control strategies discussed

Abstract

Autonomous systems face the intricate challenge of navigating unpredictable environments and interacting with external objects. The successful integration of robotic agents into real-world situations hinges on their perception capabilities, which involve amalgamating world models and predictive skills. Effective perception models build upon the fusion of various sensory modalities to probe the surroundings. Deep learning applied to raw sensory modalities offers a viable option. However, learning-based perceptive representations become difficult to interpret. This challenge is particularly pronounced in soft robots, where the compliance of structures and materials makes prediction even harder. Our work addresses this complexity by harnessing a generative model to construct a multi-modal perception model for soft robots and to leverage proprioceptive and visual information to anticipate and interpret contact interactions with external objects. A suite of tools to interpret the perception model is furnished, shedding light on the fusion and prediction processes across multiple sensory inputs after the learning phase. We will delve into the outlooks of the perception model and its implications for control purposes.