A Probabilistic Representation of User Intent for Assistive Robots

Matthew Derry1 and Brenna Argall2

I. INTRODUCTION

Many situations exist in which the human control of arobot or machine can be difficult. For individuals with cer-tain motor impairments—like spasticity or paralysis—drivingpowered wheelchairs can be challenging if not impossible[9], navigating telepresence robots is made all the moredifficult by the reduced embodiment experienced by the user,and controlling machines that have more degrees of freedom(DoF) than the control interface, (e.g. a 6-DoF robot armwith a 3-DoF joystick) can be cumbersome. These situationscan all benefit from introducing automation to assist the userin accomplishing difficult tasks. This assistance can comein many forms, from corrective inputs augmenting the userinput to the complete assumption of control by the automatedprocess. Understanding when and how much assistance toprovide can be enhanced by an accurate estimate of the goalthe the human might be trying to achieve. Research intouser intent prediction and goal estimation aims to addressthis problem, which remains an open research area.

In order to produce an estimate of a user’s intended goal,the goal hypotheses can come from analyzing the environ-ment [6], modelling the user’s control inputs to predict whatcontrols they might issue to reach a goal [7], [8], usinginverse optimal control to learn a cost function that capturesthe user’s behavior and comparing plans to different goalsusing the learned cost function [5], or confidence metricsbased on inverse models of robot state [2].

In our proposed framework, we combine the environmen-tal structure via Voronoi graphs [1], which provide a globalcontext by identifying homotopically distinct paths to globalgoals, with a general local driving model to identify theintended local subgoals represented by nodes in the Voronoigraph. In this preliminary work we have implemented thelocal model as a Gaussian Mixture Model (GMM), and useGaussian Mixture Regression (GMR) to estimate the proba-bility that a local goal is the target, given some input (e.g.user commands or robot state). The result is a probabilisticrepresentation of the goals a user might be trying to achieve.Prior research in this area has both used graph representationsof the environment as well as driving models of individualusers [3]. One aspect in which our work differs is in thedevelopment of a general, user-agnostic driving model for

1M. Derry is with the Department of Electrical Engineering andComputer Science, Northwestern University, Evanston, IL 60208, USAmderry@u.northwestern.edu

2B. Argall is jointly appointed with the Faculty of Electrical Engineeringand Computer Science, Northwestern University, Evanston, IL 60208, USAand the Rehabilitation Institute of Chicago, Chicago, IL 60611, USAbrenna.argall@northwestern.edu

local goals, combined with a graph representation of theenvironment for global context. The result is a frameworkfor estimating the intended goal of a user without requiringtraining for specific individuals or an a priori map.

This work is developed specifically in support of researchin the assistive control of powered wheelchairs. In thiscontext, global goals might include doorways for traversing,tables for docking or ramps for boarding a vehicle in additionto many others. The perception of doorways and safe dockinglocations at tables has been developed as a part of the largerscope of our smart wheelchair research [4]. Our frameworkwould be appropriate to extend to other domains such astelepresence robots or robot arm teleoperation.

II. PROPOSED FRAMEWORK AND PROOF OF CONCEPT

Our framework consists of two major components: theglobal context provided by a Voronoi graph of the currentmap, and a local model for estimating the probability that alocal subgoal (i.e. any Voronoi node adjacent to the Voronoicell occupied by the robot) is the intended subgoal. The localsubgoals then serve as the starting points of a search of theVoronoi graph in which the global goals are the end points.

We evaluated the algorithm in a simulated environmentmirroring a lab setting (Figure 2). Four global goals wereplaced in the scene, two in doorways and two at desks. Theuser was asked to drive to a particular goal and the estimatedintended goal was recorded during the task execution.

A. Global Context

Paths are homotopically distinct when there is no con-tinuous transformation between them. This property allowsfor a more compact representation of meaningful paths anddecision points (i.e. Voronoi nodes) without encoding theinfinite number of paths that can occur between two points.

Voronoi graphs provide a convenient method for identi-fying homotopically distinct paths that exist in a map. Theedges of Voronoi graphs are equidistant from the two nearestobstacles, and the nodes of Voronoi graphs are equidistantfrom the three nearest obstacles. Areas enclosed by Voronoiedges are known as Voronoi regions. Figure 2 inset showsan example of a Voronoi graph with the goals inserted in tothe graph, shown as green dots.

B. Local Model

The local model in our framework is built as a GaussianMixture Model (GMM). A weighted mixture of Gaussiandistributions is fit to a series of training data ξ = [ξi; ξo].

P (ξ|θ) =

K∑k=1

wkN (ξi, ξo;θk) (1)

Fig. 1. Ego-centric coordinates ξ of goal g used in the proof-of-concept,local model formulation. Curvature is calculated using the commandedrotational velocity ω and commanded linear velocity v.

where ξi is the input, ξo is the output, K is the numberof Gaussian components and θk contains the prior w, meanµ, and covariance Σ for the kth Gaussian component. Theparameters θ are estimated with Expectation Maximizationon a dataset of expert demonstrations driving to several goals.

With the parameters of the GMM estimated, GMR is usedto compute the resulting conditional probability distribution,which determines the likelihood that the output data ξo wasgenerated by the GMM, given the input data ξi:

P (ξo|ξi;θ) ∼ N (µ, Σ) (2)

In our proof-of-concept formulation for the local model,the input data ξi is the egocentric position of a subgoal(Figure 1), which contains the distance from the robot to thesubgoal ξdi , the difference between the robot heading andthe heading to the subgoal ξψi , and the difference betweenthe robot heading and the subgoal orientation ξφi . The outputdata ξo, is the current user-commanded curvature.

We assessed this proof-of-concept formulation in a pilotuser study with 5 participants, each performing 12 trials.The results of this pilot study were encouraging, with theestimate of the user’s goal frequently matching the truegoal. However, the study also highlighted many areas forimprovement. For example, when close to local subgoals, theuser does not actually tend to drive to the subgoal with muchprecision since the subgoal, being a nearby Voronoi node, isan artificial construct not necessarily apparent to the user,whereas the global goal—such as a doorway—is a naturalgoal that could be perceived by both user and robot. Thiscauses the local model to give the intended local subgoala low probability due to the large swing in the heading tosubgoal component of the input data, resulting in undesirablebehavior near the Voronoi edges.

An alternative formulation which we are currently devel-oping is to use the distance to the goal as the input dataξi, and as the output data ξo the heading to goal and head-ing of goal components of the egocentric polar coordinatetransformation of the local subgoal. The hypothesis is thatthe model will encode the high variance in the headingparameters seen when close to the subgoal, which can thenbe leveraged in the probability computation. An additionalbenefit is that by removing the user-commanded curvaturefrom the formulation, the number of demonstrations requiredis reduced considerably. The trade-off is that the intentestimation will lag slightly since the probabilities will updateonly after the robot moves.

Fig. 2. A simulated lab environment, inset with a voronoi segmentation.

C. Combining the Local Model with Global Context

The last step is to apply the probability distribution overlocal subgoals to the global goals. In our proof-of-conceptformulation, the shortest path from the current voronoi regionto each global goal is found. Then each global goal is simplyassigned the probability of the local subgoal that starts theshortest path to that global goal. Our pilot study highlightedthe issue that this formulation suffered poor performancewhen the user’s chosen path did not correspond to theshortest path along the Voronoi edges. To address this, analternative formulation currently under development is tobuild a true probability distribution weighted by path lengthover all paths to a global goal.

III. CONCLUSION

Accurate estimation of user intent has the potential to im-prove the performance of assistive technologies that automatesome portion of task execution. This work presents a frame-work that combines global context about the environmentwith a local probabilistic model to make estimates aboutuser intent. This initial work has shown both promise anddirections for improvement. The alternative formulations forthe local model and application to the global goals, identi-fied in Section II, are under active development, and theirevaluation will be included in the workshop presentation.

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