TacMamba: A Tactile History Compression Adapter Bridging Fast Reflexes and Slow VLA Reasoning
Zhenan Wang, Yanzhe Wang, Meixuan Ren, Peng Li, Yang Liu, Yifei Nie, Limin Long, Yun Ye, Xiaofeng Wang, Zhen Zhu, Huixu Dong

TL;DR
TacMamba introduces a hierarchical tactile-visual architecture that effectively fuses high-frequency tactile data with low-frequency visual planning, enabling real-time manipulation with improved success rates.
Contribution
It presents a novel tactile interface, a Mamba-based history compressor with ultra-low latency, and a dual-stage training strategy for effective tactile-visual integration in manipulation tasks.
Findings
Achieves 100% success in discrete counting and state switching tasks.
Outperforms visual-only baselines significantly.
Operates within strict real-time constraints.
Abstract
In visually ambiguous manipulation such as detecting button click tactile feedback is often the sole source of ground truth. However, fusing tactile data poses a significant challenge due to a spatiotemporal mismatch: tactile perception requires high-frequency processing with long-horizon memory (System 1), whereas visual policies operate at low control frequencies (System 2). Existing architectures struggle to bridge this gap: Transformers are computationally prohibitive for high-frequency loops (>100Hz), while LSTMs suffer from forgetting over extended interaction histories. In this paper, we introduce TacMamba, a hierarchical architecture that aligns high-bandwidth tactile reflexes with low-frequency visual planning. Our approach comprises three core contributions: (1) a custom high-frequency tactile interface designed for flexible integration; (2) a Mamba-based Tactile History…
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Taxonomy
TopicsAdvanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Neural Networks and Reservoir Computing
