SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection
This research presents a new multimodal approach for slip detection in robotics, achieving high accuracy and low latency, which is relevant for improving the performance of robotic systems.
SlipSense is a new multimodal tactile learning framework for detecting slips in robotic manipulation.
It uses a compact $32 imes 32$ piezoresistive array and a 3-axis accelerometer to capture spatial pressure and friction-induced vibrations.
The system achieves 96.7% Macro F1 with a false-positive rate below 1.
6%, detecting 76% of slip events within 23.1 ms.
Experiments on 1.4 million frames show it generalizes zero-shot to a Tesollo dexterous hand.
Slip detection is fundamental to dexterous manipulation, yet existing systems often lack precise characterization of detection latency and cross-platform generalization. We present SlipSense, a multimodal tactile slip-detection framework built on TacV5, a compact sensor integrating a $32 \times 32$ piezoresistive array operating at 240 Hz and a 3-axis MEMS accelerometer operating at 8 kHz. The piezoresistive array captures spatial pressure distributions, while the accelerometer captures friction-induced vibrations, providing complementary slip cues. The framework performs modality-specific encoding, intra-sensor fusion, and cross-modal attention with causal temporal prediction at 240 Hz. Experiments on a dataset of 1.4 million frames spanning 37 objects demonstrate the complementarity of the two modalities. SlipSense achieves 96.7% Macro F1 with a false-positive rate below 1.6%, detecting 76% of slip events within 23.1 ms. When trained solely on UMI data, SlipSense generalizes zero-shot to a Tesollo dexterous hand, transferring across unseen objects, distinct sensor units, and robotic platforms without retraining.
This report is categorized under AITOP's "Models" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include multimodal, tactile, robotics, slip detection.
Source attribution: curated from arXiv cs.AI (@Tong Jian, Aditya Thurvas Senthil Kumar, Xinyi Li, Ziling Chen, Tianyu Dai, Ali Sengul, Matteo Grimaldi, Wenjie Lu, Saleh Nabi, Tao Yu).
