Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots

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Learning quadrupedal locomotion over challenging terrain (2020, neutral): “L =  i∈dim(X\contactstates) ( m i − m i gt ) 2 ─ 2  i 2 + log(  i ) (7)”
Contact Model Fusion for Event-Based Locomotion in Unstructured Terrains (2018, background): “Camurri et al. [6] used approximate inverse-dynamics-based forces as inputs to a contact probability prior for determining when a foothold was secure enough for use in base-velocity estimation.”
Dynamic Walking with Compliance on a Cassie Bipedal Robot (2019, methodology): “with assumed knowledge of the contact surface has been used in a mixed integer Quadratic Program (QP) [26] for handling contacts, and others have looked at predicting covariance values for contact velocities through contact force [5].”
Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality (2018, methodology): “hy W ext 2R6 rather than W ext 2F6 [49] 25The twist (i.e. 6D spatial velocity) is usually computed by a state estimator, which merges Inertial Measurement Unit (imu), encoder and force/torque sensors [19]. 26It is well known that it is impossible to distinguish between a CoM offset and an external w”