Human Motion Intent Inferencing in Teleoperation Through a SINDy Paradigm

TL;DR

This paper introduces Psychic, a novel framework that detects sudden intent changes in teleoperation by modeling operator motions with stochastic differential equations and SINDy, enabling early goal detection and inference in unstructured scenarios.

Contribution

The paper presents Psychic, a new method combining jump-drift-diffusion models and SINDy to infer operator intent and detect goal transitions in teleoperation tasks.

Findings

Successfully detects sudden goal jumps in operator trajectories.

Enables early detection of goal changes and discovery of new goals.

Outperforms traditional likelihood-based models in retrospective analysis.

Abstract

Intent inferencing in teleoperation has been instrumental in aligning operator goals and coordinating actions with robotic partners. However, current intent inference methods often ignore subtle motion that can be strong indicators for a sudden change in intent. Specifically, we aim to tackle 1) if we can detect sudden jumps in operator trajectories, 2) how we appropriately use these sudden jump motions to infer an operator's goal state, and 3) how to incorporate these discontinuous and continuous dynamics to infer operator motion. Our framework, called Psychic, models these small indicative motions through a jump-drift-diffusion stochastic differential equation to cover discontinuous and continuous dynamics. Kramers-Moyal (KM) coefficients allow us to detect jumps with a trajectory which we pair with a statistical outlier detection algorithm to nominate goal transitions. Through identifying jumps, we can perform early detection of existing goals and discover undefined goals in unstructured scenarios. Our framework then applies a Sparse Identification of Nonlinear Dynamics (SINDy) model using KM coefficients with the goal transitions as a control input to infer an operator's motion behavior in unstructured scenarios. We demonstrate Psychic can produce probabilistic reachability sets and compare our strategy to a negative log-likelihood model fit. We perform a retrospective study on 600 operator trajectories in a hands-free teleoperation task to evaluate the efficacy of our opensource package, Psychic, in both offline and online learning.