The Manifold Particle Filter for State Estimation on High-dimensional Implicit Manifolds
This work addresses state estimation in robotics during contact interactions, offering incremental improvements by extending existing MPF methods to higher dimensions with implicit representations.
The authors tackled state estimation for a noisy robot arm and underactuated hand using an Implicit Manifold Particle Filter (MPF) that leverages touch sensors to represent contact manifolds implicitly via signed distance fields, enabling extension to higher dimensions (six or more) and showing faster convergence and greater accuracy than conventional particle filters during persistent contact.
We estimate the state a noisy robot arm and underactuated hand using an Implicit Manifold Particle Filter (MPF) informed by touch sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us to extend the MPF to higher (six or more) dimensional state spaces. Earlier work (which explicitly represents the contact manifold) only shows the MPF in two or three dimensions. Through a series of experiments, we show that the implicit MPF converges faster and is more accurate than a conventional particle filter during periods of persistent contact. We present three methods of sampling the implicit contact manifold, and compare them in experiments.