Structured Bayesian Meta-Learning for Data-Efficient Visual-Tactile Model Estimation

Abstract

Estimating visual-tactile models of deformable objects is challenging because vision suffers from occlusion, while touch data is sparse and noisy. We propose a novel data-efficient method for dense heterogeneous model estimation by leveraging experience from diverse training objects. The method is based on Bayesian Meta-Learning (BML), which can mitigate overfitting high-capacity visual-tactile models by meta-learning an informed prior and naturally achieves few-shot online estimation via posterior estimation. However, BML requires a shared parametric model across tasks but visual-tactile models for diverse objects have different parameter spaces. To address this issue, we introduce Structured Bayesian Meta-Learning (SBML) that incorporates heterogeneous physics models, enabling learning from training objects with varying appearances and geometries. SBML performs zero-shot vision-only prediction of deformable model parameters and few-shot adaptation after a handful of touches. Experiments show that in two classes of heterogeneous objects, namely plants and shoes, SBML outperforms existing approaches in force and torque prediction accuracy in zero- and few-shot settings.