Why Modeling Human Haptic Material Perception with AI Is Difficult

TL;DR

This paper discusses the challenges and opportunities in modeling human haptic material perception with AI, emphasizing data scarcity, evaluation standards, and model limitations.

Contribution

It identifies key bottlenecks in AI-haptics research and reviews strategies to overcome them for better understanding and modeling of human touch.

Findings

Haptic datasets are scarce and lack diversity.

Standardized benchmarks for tactile perception are missing.

Model interpretability remains a significant challenge.

Abstract

Touch plays a central role in how humans perceive and recognize materials through physical contact. Despite decades of research, the mechanisms by which tactile signals are transformed into meaningful perceptual representations remain poorly understood, limiting the design of interactive systems and intelligent agents with human-like haptic perception. Recent advances in artificial intelligence (AI) offer new opportunities to model and exploit tactile data; however, haptics presents fundamental challenges for contemporary AI due to its interaction-dependent, multimodal nature. This position paper argues that progress at the intersection of AI and haptics is constrained by three key bottlenecks: (1) the scarcity of large, diverse, and balanced haptic datasets; (2) the lack of standardized evaluation platforms and perceptual benchmarks; and (3) limitations in model capacity and interpretability when applied to tactile perception. I discuss how these challenges impede generalization, reproducibility, and scientific insight into human touch and review emerging strategies to address them. This paper highlights opportunities for coordinated, cross-disciplinary efforts to advance AI systems that not only perform robust haptic perception but also contribute to a deeper understanding of human touch.