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.
Modifying motion capture to satisfy the constraints of new animation is difficult when contact is involved, and a critical problem for animation of hands. The compliance with which a character makes contact also reveals important aspects of the movement's purpose. We present a new technique called interaction capture, for capturing these contact phenomena. We capture contact forces at the same time as motion, at a high rate, and use both to estimate a nominal reference trajectory and joint compliance. Unlike traditional methods, our method estimates joint compliance without the need for motorized perturbation devices. New interactions can then be synthesized by physically based simulation. We describe a novel position-based linear complementarity problem formulation that includes friction, breaking contact, and the compliant coupling between contacts at different fingers. The technique is validated using data from previous work and our own perturbation-based estimates.  Consumer-grade, real-time motion capture devices are becoming commonplace in every household, thanks to the recent development in depth-camera technologies. We introduce a new approach to capturing and reconstructing freeform, full-body human motion using force sensors, supplementary to existing, consumer-grade mocap systems. Our algorithm exploits the dynamic aspects of human movement, such as linear and angular momentum, to provide key information for full-body motion reconstruction. Using two pressure sensing platforms (Wii Balance Board) and a hand tracking device, we demonstrate that human motion can be largely reconstructed from ground reaction forces along with a small amount of arm movement information.