SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots

2026-06-23

AI Summary

This study addresses the challenge of online, high-sensitivity early slip detection in legged robots, which often suffer from instability on slippery terrain due to insufficient perceptual capabilities. The authors propose SlipSense, a novel framework that, for the first time, integrates a custom force-sensing footpad with a lightweight LSTM model to enable vision-free, real-time estimation of ground reaction forces and identification of slip anomalies. By leveraging multimodal sensing, temporal modeling, and online anomaly detection, SlipSense achieves high-precision perception of incipient slips. Evaluated on the Unitree Go1 platform, the method attains a slip detection displacement accuracy of 24.1 ± 6.4 mm and an accuracy of 85.9%, representing a 3.3-fold improvement in resolution and a 24% relative gain in accuracy over prior approaches, thereby laying the foundation for force-driven gait adaptation.

Abstract

Legged robots rely on accurate ground interaction awareness to traverse variable terrains, such as slippery surfaces. Existing slip detection methods often rely on kinematics and proprioception, which lack the sensitivity to detect early-stage slips that occur prior to catastrophic instability. Thus, this paper presents SlipSense, a novel framework for online force-based slip detection using a custom lightweight sensorized foot for quadrupeds to detect slip. The framework integrates a multimodal sensor design with a LSTM-based model to infer ground reaction forces and detect slip-indicative anomalies during locomotion. The proposed framework is deployed on a Unitree Go1 quadruped to demonstrate blind online slip detection over a slippery terrain. Our method detects early-stage slips down to an average displacement of 24.1 +/-6.4mm with an overall accuracy of 85.9%. This represents a 3.3-fold finer detection resolution and a 24% relative accuracy improvement over a standard kinematic baseline that uses foot velocity inferred through state estimation. The work in this paper serves as a foundation for force-aware gait adaptation in legged robotic locomotion, allowing future controllers to estimate terrain friction and adjust constraints, thus improving the overall stability of the system.

Problem

• slip detection
• legged robots
• ground interaction awareness
• early-stage slip
• terrain friction

Innovation

• multimodal sensing
• slip detection
• force estimation
• legged robots
• LSTM-based model