J. Joe Payne

Ravi Tejwani, Karl Velazquez, John Payne et al.

A method for cross-modality embedding of force profile and words is presented for synergistic coordination of verbal and haptic communication. When two people carry a large, heavy object together, they coordinate through verbal communication about the intended movements and physical forces applied to the object. This natural integration of verbal and physical cues enables effective coordination. Similarly, human-robot interaction could achieve this level of coordination by integrating verbal and haptic communication modalities. This paper presents a framework for embedding words and force profiles in a unified manner, so that the two communication modalities can be integrated and coordinated in a way that is effective and synergistic. Here, it will be shown that, although language and physical force profiles are deemed completely different, the two can be embedded in a unified latent space and proximity between the two can be quantified. In this latent space, a force profile and words can a) supplement each other, b) integrate the individual effects, and c) substitute in an exchangeable manner. First, the need for cross-modality embedding is addressed, and the basic architecture and key building block technologies are presented. Methods for data collection and implementation challenges will be addressed, followed by experimental results and discussions.

J. Joe Payne, Nathan J. Kong, Aaron M. Johnson

In this paper we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimation. Examining how guard surface uncertainty affects the time spent in each mode, we derive a guard saltation matrix - which maps perturbations prior to hybrid events to perturbations after - accounting for additional variation in the resulting state. Additionally, we propose the use of parameterized reset functions - capturing how unknown parameters change how states are mapped from one mode to the next - the Jacobian of which accounts for the additional uncertainty in the resulting state. The accuracy of these mappings is shown by simulating sampled distributions through uncertain transition events and comparing the resulting covariances. Finally, we integrate these additional terms into the "uncertainty aware Salted Kalman Filter", uaSKF, and show a peak reduction in average estimation error by 24-60% on a variety of test conditions and systems.

Nathan J. Kong, J. Joe Payne, George Council et al.

Many state estimation and control algorithms require knowledge of how probability distributions propagate through dynamical systems. However, despite hybrid dynamical systems becoming increasingly important in many fields, there has been little work on utilizing the knowledge of how probability distributions map through hybrid transitions. Here, we make use of a propagation law that employs the saltation matrix (a first-order update to the sensitivity equation) to create the Salted Kalman Filter (SKF), a natural extension of the Kalman Filter and Extended Kalman Filter to hybrid dynamical systems. Away from hybrid events, the SKF is a standard Kalman filter. When a hybrid event occurs, the saltation matrix plays an analogous role as that of the system dynamics, subsequently inducing a discrete modification to both the prediction and update steps. The SKF outperforms a naive variational update - the Jacobian of the reset map - by having a reduced mean squared error in state estimation, especially immediately after a hybrid transition event. Compared a hybrid particle filter, the particle filter outperforms the SKF in mean squared error only when a large number of particles are used, likely due to a more accurate accounting of the split distribution near a hybrid transition.