Human Motion Intent Inferencing in Teleoperation Through a SINDy Paradigm

2025-11-11

AI Summary

To address the challenge of inferring human motion intent—particularly the early detection of abrupt intent transitions—in teleoperation, this paper proposes Psychic, a novel probabilistic framework. First, operator trajectory dynamics are modeled using a jump-diffusion stochastic differential equation that captures both continuous evolution and discontinuous jumps. Second, Kramers–Moyal coefficient estimation is integrated with statistical anomaly detection to precisely identify abrupt jump events in real time. Third, sparse nonlinear dynamics identification (SINDy) is employed to model target-state transitions, enabling probabilistic prediction for both known and previously unseen targets. The framework supports both offline modeling and online adaptation. Evaluated on 600 real-world teleoperation trajectories, Psychic generates probabilistic reachable sets and achieves significantly lower negative log-likelihood than baseline methods. An open-source implementation is released for practical deployment in teleoperation systems.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Detecting sudden jumps in operator trajectories during teleoperation
• Using jump motions to infer operator's goal state transitions
• Modeling discontinuous and continuous dynamics for motion inference

Innovation

• Uses jump-drift-diffusion equation for motion dynamics
• Applies SINDy model with Kramers-Moyal coefficients
• Detects goal transitions via outlier detection algorithm