SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots
For legged robot locomotion, this provides a more sensitive slip detection method that enables early intervention before catastrophic instability, though it is an incremental improvement over existing kinematic approaches.
SlipSense introduces a multimodal sensorized foot and LSTM-based model for online slip detection in legged robots, achieving early-stage slip detection at 24.1mm displacement with 85.9% accuracy, a 3.3x finer resolution and 24% relative improvement over kinematic baselines.
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.