Learn the basics of Bayesian inference and how it can be used for inverse kinematics.
Title: Inverse Kinematics: A Bayesian Versus Least-Squares Approach
Speaker: Todd Pataky, Kyoto University
Time: Monday, July 20, 2020 4:00 p.m. Pacific Time
Registration: Registration is free but required, as space is limited. Click here to register.
When estimating joint kinematics from a set of noisy marker measurements, most inverse kinematics (IK) approaches aim to minimize errors by distributing them amongst the markers in a least-squares sense. This talk will describe how Bayesian calculations can be used to maximize the probability that a specific set of joint angles would produce the observed marker positions. This computation of marker positions from joint angles is referred to as a forward kinematics model or forward model. Preliminary Bayesian IK results show order-of-magnitude improvement over least-squares estimates, with Bayesian IK outperforming least-squares IK in over 90% of a large number of random simulations involving planar rotations, with an approximately tenfold decrease in rotation estimate error. This talk will assume no knowledge of Bayesian statistics, and will work from fundamental Bayesian concepts to simple IK models, then finally to explain why Bayesian IK appears to outperform least-squares approaches.