Author name cluster

Mark Zolotas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

2 author rows

ICRA Conference 2025 Conference Paper

Data-Driven Sampling Based Stochastic MPC for Skid-Steer Mobile Robot Navigation

Ananya Trivedi
Sarvesh Prajapati
Anway Shirgaonkar
Mark Zolotas
Taskin Padir

Traditional approaches to motion modeling for skid-steer robots struggle to capture nonlinear tire-terrain dynamics, especially during high-speed maneuvers. In this paper, we tackle such nonlinearities by enhancing a dynamic unicycle model with Gaussian Process (GP) regression outputs. This enables us to develop an adaptive, uncertainty-informed navigation formulation. We solve the resultant stochastic optimal control problem using a chance-constrained Model Predictive Path Integral (MPPI) control method. This approach formulates obstacle avoidance and path-following as chance constraints, accounting for residual uncertainties from the GP to ensure safety and reliability in control. Leveraging GPU acceleration, we efficiently manage the non-convex nature of the problem, ensuring real-time performance. Our approach unifies path-following and obstacle avoidance across different terrains, unlike prior works which typically focus on one or the other. We compare our GP-MPPI method against unicycle and data-driven kinematic models within the MPPI framework. In simulations, our approach shows superior tracking accuracy and obstacle avoidance. We further validate our approach through hardware experiments on a skid-steer robot platform, demonstrating its effectiveness in high-speed navigation. The GPU implementation of the proposed method and supplementary video footage are available at https://stochasticmppi.github.io.

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ICRA Conference 2024 Conference Paper

A Probabilistic Motion Model for Skid-Steer Wheeled Mobile Robot Navigation on Off-Road Terrains

Ananya Trivedi
Mark Zolotas
Adeeb Abbas
Sarvesh Prajapati
Salah Bazzi
Taskin Padir

Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skidding and slipping. In this work, using Gaussian Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear effects of tire-terrain interaction on robot velocities in a probabilistic fashion. Using the mean estimates from GPR, we propose a data-driven dynamic motion model that is more accurate at predicting future robot poses than conventional kinematic motion models. By efficiently solving a convex optimization problem based on the history of past robot motion, the GPR augmented motion model generalizes to previously unseen terrain conditions. The output distribution from the proposed motion model can be used for local motion planning approaches, such as stochastic model predictive control, leveraging model uncertainty to make safe decisions. We validate our work on a benchmark real-world multi-terrain SSWMR dataset. Our results show that the model generalizes to three different terrains while significantly reducing errors in linear and angular motion predictions. As shown in the attached video, we perform a separate set of experiments on a physical robot to demonstrate the robustness of the proposed algorithm.

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IROS Conference 2024 Conference Paper

A Voxel-Enabled Robotic Assistant for Omnidirectional Conveyance

Michael Angelo Carvajal
Katiso Mabulu
Muneer Lalji
James Flanagan
Rui Luo 0005
Samuel Hibbard
Tanav Chinthapatla
Rohan Bettadpur

Conventional bidirectional conveyance platforms use a flat translating belt or a series of spinning wheels or rollers to apply a shear force to payloads to move them. Wheel/roller-based conveyors in particular cannot double as a worktop when idle, do not support collision-free multi-object manipulation by default, and are not optimized to move objects that are either slippery or pliable—let alone both. This paper introduces a Voxel-Enabled Robotic Assistant (VERA), a network of intelligent table "partitions" whose topologically dynamic worktops enable omnidirectional conveyance; each partition is composed of a 2D array of "quadrants, " axisymmetric modules that can be hot-swapped for maintenance or repairs; each quadrant contains a 2D array of "cells, " unitary robotic submodules; each cell houses an independently controllable "voxel, " the motorized rotary element that conveys an overhead object. The efficacy of a VERA prototype was determined by evaluating waypoint error as a range of payloads were maneuvered between trajectory waypoints. By conveying both pliable and rigid payloads having slippery textures, the faceted voxels outperformed those augmented to mimic the circular-profiled wheels/rollers of competitor systems. VERA also successfully performed collision-free multi-object planar manipulations planned by its pathfinding algorithm. In light of these results, VERA emerges as a promising material handling platform for use in "Future of Work" settings as the need for multi-purpose collaborative industrial robots continues to grow.

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IROS Conference 2024 Conference Paper

User-customizable Shared Control for Robot Teleoperation via Virtual Reality

Rui Luo 0005
Mark Zolotas
Drake Moore
Taskin Padir

Shared control can ease and enhance a human operator’s ability to teleoperate robots, particularly for intricate tasks demanding fine control over multiple degrees of freedom. However, the arbitration process dictating how much autonomous assistance to administer in shared control can confuse novice operators and impede their understanding of the robot’s behavior. To overcome these adverse side-effects, we propose a novel formulation of shared control that enables operators to tailor the arbitration to their unique capabilities and preferences. Unlike prior approaches to "customizable" shared control where users could indirectly modify the latent parameters of the arbitration function by issuing a feedback command, we instead make these parameters observable and directly editable via a virtual reality (VR) interface. We present our user-customizable shared control method for a teleoperation task in SE(3), known as the buzz wire game. A user study is conducted with participants teleoperating a robotic arm in VR to complete the game. The experiment spanned two weeks per subject to investigate longitudinal trends. Our findings reveal that users allowed to interactively tune the arbitration parameters across trials generalize well to adaptations in the task, exhibiting improvements in precision and fluency over direct teleoperation and conventional shared control.

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IROS Conference 2022 Conference Paper

Disentangled Sequence Clustering for Human Intention Inference

Mark Zolotas
Yiannis Demiris

Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probability distribution of “intent” conditioned on the robot's perceived state. However, these approaches typically assume task-specific labels of human intent are known a priori. To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner. The proposed framework leverages recent advances in unsupervised learning to disentangle latent representations of sequence data, separating time-varying local features from time-invariant global attributes. As a novel extension, the DiSCVAE also infers a discrete variable to form a latent mixture model and thus enable clustering over these global sequence concepts, e. g. high-level intentions. We evaluate the DiSCVAE on a real-world human-robot interaction dataset collected using a robotic wheelchair. Our findings reveal that the inferred discrete variable coincides with human intent, holding promise for collaborative settings, such as shared control.

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IJCAI Conference 2020 Conference Paper

Transparent Intent for Explainable Shared Control in Assistive Robotics

Mark Zolotas
Yiannis Demiris

Robots supplied with the ability to infer human intent have many applications in assistive robotics. In these applications, robots rely on accurate models of human intent to administer appropriate assistance. However, the effectiveness of this assistance also heavily depends on whether the human can form accurate mental models of robot behaviour. The research problem is to therefore establish a transparent interaction, such that both the robot and human understand each other’s underlying "intent". We situate this problem in our Explainable Shared Control paradigm and present ongoing efforts to achieve transparency in human-robot collaboration.

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IROS Conference 2019 Conference Paper

Towards Explainable Shared Control using Augmented Reality

Mark Zolotas
Yiannis Demiris

Shared control plays a pivotal role in establishing effective human-robot interactions. Traditional control-sharing methods strive to complement a human’s capabilities at safely completing a task, and thereby rely on users forming a mental model of the expected robot behaviour. However, these methods can often bewilder or frustrate users whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. To resolve this model misalignment, we introduce Explainable Shared Control as a paradigm in which assistance and information feedback are jointly considered. Augmented reality is presented as an integral component of this paradigm, by visually unveiling the robot’s inner workings to human operators. Explainable Shared Control is instantiated and tested for assistive navigation in a setup involving a robotic wheelchair and a Microsoft HoloLens with add-on eye tracking. Experimental results indicate that the introduced paradigm facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment.

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IROS Conference 2018 Conference Paper

Head-Mounted Augmented Reality for Explainable Robotic Wheelchair Assistance

Mark Zolotas
Joshua Elsdon
Yiannis Demiris

Robotic wheelchairs with built-in assistive features, such as shared control, are an emerging means of providing independent mobility to severely disabled individuals. However, patients often struggle to build a mental model of their wheelchair's behaviour under different environmental conditions. Motivated by the desire to help users bridge this gap in perception, we propose a novel augmented reality system using a Microsoft Hololens as a head-mounted aid for wheelchair navigation. The system displays visual feedback to the wearer as a way of explaining the underlying dynamics of the wheelchair's shared controller and its predicted future states. To investigate the influence of different interface design options, a pilot study was also conducted. We evaluated the acceptance rate and learning curve of an immersive wheelchair training regime, revealing preliminary insights into the potential beneficial and adverse nature of different augmented reality cues for assistive navigation. In particular, we demonstrate that care should be taken in the presentation of information, with effort-reducing cues for augmented information acquisition (for example, a rear-view display) being the most appreciated.

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