Transparent Intent for Explainable Shared Controlin Assistive Robotics

Mark Zolotas and Yiannis DemirisDept. of Electrical and Electronic Engineering, Imperial College London, UK

{mark.zolotas12, y.demiris}@imperial.ac.uk

Abstract

Robots supplied with the ability to infer human in-tent have many applications in assistive robotics.In these applications, robots rely on accurate mod-els of human intent to administer appropriate assis-tance. However, the effectiveness of this assistancealso heavily depends on whether the human canform accurate mental models of robot behaviour.The research problem is to therefore establish atransparent interaction, such that both the robot andhuman understand each other’s underlying “intent”.We situate this problem in our Explainable SharedControl paradigm and present ongoing efforts toachieve transparency in human-robot collaboration.

1 IntroductionEquipping robots with the capability to infer human intentfrom observed behaviour holds immense potential for assis-tive robotics [Demiris, 2007]. For instance, in shared controltasks where a human and robot collaborate towards a com-mon goal, the synergy will be more effective if the robot’s ac-tions are in agreement with their partner’s intentions. Never-theless, the quality of assistance administered through sharedcontrol also depends on whether the rationale behind robotbehaviour is clearly explicated [Abbink et al., 2018]. When-ever robot actions do not align with a person’s mental mod-els of expected system behaviour, there is risk of causing ob-struction and frustration rather than support. A means of rec-onciling mismatched agent models is therefore required.

Within explainable AI and specifically planning, the prob-lem of “model reconciliation” is regarded as a process of ex-planation [Fox et al., 2017; Chakraborti et al., 2017]. Ex-planations here refer to model updates that help resolve anydifferences between a human’s expectations of robot plansand their actual representations. One way of generating theseexplanations is to expose internal representations to humanobservers, for which augmented reality (AR) is an increas-ingly prevalent choice of medium [Walker et al., 2018].

Whilst explainable AI is a widely explored area of interest,it has only recently garnered similar attention in shared con-trol [Zolotas et al., 2018; Zolotas and Demiris, 2019]. Never-theless, bringing explainability to shared control poses new

Intention Inference

Assistive ControlEstimated

Intent

Sensory Inputs

Motor Commands

Robot Internal States

Augmented Reality

Visual Explanations

Figure 1: Standard shared control (black arrows) will involve aninference of the user’s task intent based on sensory inputs, followedby an assistive control process that outputs motor commands to therobot, e.g. a robotic wheelchair. In Explainable Shared Control (redarrows), the robot’s internal states are also visually represented inaugmented reality, i.e. making robot “intent” transparent.

challenges, namely the dependence on a continuous com-munication channel, the requirement for implicit signals ofintent, and the overall lack of consensus on guidelines forshared control [Abbink et al., 2018]. We introduced the Ex-plainable Shared Control (XSC) paradigm to address thesechallenges and resolve model misalignment by using AR vi-sualisations as explanations [Zolotas and Demiris, 2019].

The key perspective behind XSC is transparency, wherethe objective is to construct a communication bridge of intentbetween human and robot (see Fig. 1). This paper elaborateson XSC by outlining our contributions to “transparent intent”and providing outlooks on how the continuous interplay oftransmitting intentions could allow online model updates.

2 Transparent IntentThere are two perspectives of another’s intent in shared con-trol: the human’s and the robot’s.

2.1 Human Perspective of the InteractionNumerous modes of sensory feedback can be employed torelay information back to users. An increasingly popular in-terface for conveying robot intent during human-robot inter-

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2

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Figure 2: Augmented reality view of: (1) A dual-arm collaborativerobot and its overlaid 3D model to project intent; (2) An interactableobject; (3) An action that the user can select for the robot to perform.

action is AR [Walker et al., 2018]. In particular, we advocatefor AR headsets, as their heightened sense of presence bettercombats model mismatch.

For example, Fig. 2 illustrates how a user’s immersive viewof a dual-arm collaborative robot might be captured in AR,where affordable robot behaviours are projected as indicatorsof intent. To create this user perspective, we broadcasted real-time data originating from the robot’s perceptive and motionplanning capabilities into a concurrent AR application. Thisdata included detected object poses in the shared workspace,as well as the robot arm joint poses corresponding to differenttrajectories for manipulating objects. When users can simplyselect collaborative actions via such an AR interface and donot need to physically interact or share control with the robot,then we find this visual overlay approach to be sufficient.

However, visualising a robot’s inner workings in sharedcontrol requires more careful consideration due to the ac-tive user engagement. Our findings from a user study in-volving an AR headset integrated onto an assistive roboticwheelchair exemplified how poorly designed visualisationscan harm task performance and even cause greater misun-derstanding [Zolotas et al., 2018]. In turn, we introduced aset of interface guidelines under XSC on how to best demys-tify shared control [Zolotas and Demiris, 2019]. By deploy-ing contextual and predictive AR visual aids to augment theenvironment, users in our follow-up study exhibited quickerrecovery times from adverse events that are typically encoun-tered during model misalignment in wheelchair navigation.

2.2 Robot Perspective of the InteractionFrom the robot’s perspective, estimating human intent essen-tially revolves around deriving a model to match observedbehaviour [Demiris, 2007]. There is a vast range of litera-ture supporting this endeavour, with deep learning algorithmsbecoming commonplace due to their capacity to model high-dimensional, non-linear human behaviour conditioned on theenvironment state perceived by the robot. Although power-ful, these “black-box” algorithms are notoriously difficult tointerpret and hence explain to end-users [Fox et al., 2017].

As with the AR interface design, we produced a set of

guidelines on how to recognise human intent in a suitablemanner for user interpretation [Zolotas and Demiris, 2019].In this work, we determined that the differential-drive dy-namics of our robotic wheelchair could be used in conjunc-tion with the user’s joystick commands to yield a trajectory ofnavigational intent that could be easily depicted in AR. Thepredicted trajectories benefitted transparency by acting as in-dicators of human intent for the robot, as well as by grantingusers with a visual glimpse at the robot’s internal models.

3 Ongoing ResearchThere are multiple research avenues to pursue within XSCbefore our proposed notion of transparent intent can bolsterthe widespread use of assistive robots. To expand the scopeof this paradigm, we have made progress in an unsupervisedclustering approach for learning human intentions in taskswithout prior knowledge of the world dynamics or user goals.

Moreover, the evaluation of XSC demands that model mis-match is traced over a continuous interaction instead of solelyon a session basis. As a result, we plan to further investigatehow the frequency of mutual model updates impacts align-ment whilst the transparent interaction is unfolding.

AcknowledgementsThis research was supported by an EPSRC Doctoral TrainingAward to Mark Zolotas and a Royal Academy of EngineeringChair in Emerging Technologies to Yiannis Demiris.

References[Abbink et al., 2018] David A. Abbink, Tom Carlson, Mark

Mulder, Joost C. F. de Winter, Farzad Aminravan, Tricia L.Gibo, and Erwin R. Boer. A Topology of Shared ControlSystems—Finding Common Ground in Diversity. IEEETrans. Hum. Mach. Syst., 48(5):509–525, 2018.

[Chakraborti et al., 2017] Tathagata Chakraborti, SarathSreedharan, Yu Zhang, and Subbarao Kambhampati. PlanExplanations as Model Reconciliation: Moving beyondExplanation as Soliloquy. In IJCAI, pages 156–163, 2017.

[Demiris, 2007] Yiannis Demiris. Prediction of intent inrobotics and multi-agent systems. Cognitive Processing,8(3):151–158, 2007.

[Fox et al., 2017] Maria Fox, Derek Long, and Daniele Mag-azzeni. Explainable Planning. In IJCAI Workshop on Ex-plainable AI, 2017.

[Walker et al., 2018] Michael Walker, Hooman Hedayati,Jennifer Lee, and Daniel Szafir. Communicating RobotMotion Intent with Augmented Reality. In ACM/IEEEHRI, pages 316–324, 2018.

[Zolotas and Demiris, 2019] Mark Zolotas and YiannisDemiris. Towards Explainable Shared Control using Aug-mented Reality. In IEEE/RSJ IROS, pages 3020–3026,2019.

[Zolotas et al., 2018] Mark Zolotas, Joshua Elsdon, andYiannis Demiris. Head-Mounted Augmented Reality forExplainable Robotic Wheelchair Assistance. In IEEE/RSJIROS, pages 1823–1829, 2018.

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IntroductionTransparent IntentHuman Perspective of the InteractionRobot Perspective of the Interaction

Ongoing Research