International Conference on Metaverse Computing, Networking and Applications (MetaCom 2026)

Nov 6-10, 2026 · Xi'an, China.

http://www.metacomm.org/2026

MARC (Meta-Sejong AI Robotics Challenge) 2026 —
A Vision-Language-Action Grounding Student Challenge Workshop on Embodied Metaverse Robotics

This workshop evaluates embodied robotic autonomy in a metaverse digital-twin environment, where a robot takes natural-language commands and multi-view CCTV imagery as input and carries out Vision-Language-Action (VLA) grounding, mobile manipulation, and semantic destination routing. It is organized as a simulation-based student challenge; the paper presentations and live demonstrations by the finalist teams constitute the workshop's academic program.

Organization Chairs

• Program Chair, Jaeho Kim, Sejong University, Republic of Korea, kimjh@sejong.ac.kr
• Co-Chair, JaeSeung Song, Sejong University, Republic of Korea, jssong@sejong.ac.kr
• Co-Chair, Li Bai, Temple University, USA, li.bai@temple.edu
• Technical Chair, Taehyun Kim, Sejong University, Republic of Korea, thyun@sejong.ac.kr

Topics

MARC 2026 addresses embodied robots that understand natural-language commands and multi-view CCTV imagery to localize a target and then autonomously navigate and manipulate it, within a metaverse digital twin (a 3D model of the Sejong University campus). Participants implement an integrated chain of capabilities: natural-language command interpretation, visual grounding, mobile manipulation, and semantics-based destination routing.

Focus — Complementarity with the Main Conference

• Metaverse × Embodied AI: The workshop sits at the intersection of the conference's Computing and AI tracks, addressing embodied robotic autonomy that leverages a metaverse digital twin as a simulation training ground. Delivered as a hands-on student challenge, this robotics-in-metaverse focus complements the regular paper tracks.
• VLA Grounding: It goes beyond object detection to evaluate the integration of language, vision, and action—identifying the relevant scene and routing to the appropriate action and destination.

Timeliness

VLA and embodied AI have recently become central topics in the robotics and AI communities, and demand for reproducible, safe simulation-based evaluation environments is rising sharply. By adopting a standard ROS 2 interface on Isaac Sim 5.1.0 (LTS), this workshop secures sim-to-real transferability and offers direct relevance to the MetaCom community.

Competition Structure

The challenge runs in two stages. Stage 1 (VLA grounding) scores visual localization and reporting on five axes: camera selection, target/type identification, landmark identification, anchor (relative) position, and target-coordinate attribution. Stage 2 (mobile manipulation) has the robot autonomously navigate to and secure the Stage 1 target, then route it to the destination matching its semantic class (misdelivery is penalized).

Technical Platform

The challenge runs on NVIDIA Isaac Sim 5.1.0 (LTS) with ROS 2 Humble on Ubuntu 22.04. Locomotion uses cmd_vel (geometry_msgs/Twist; max. 1.0 m/s linear, 1.0 rad/s angular), and the manipulator uses 7-DoF joint-level control (sensor_msgs/JointState, 60 Hz). Scoring is split evenly (50:50) between Stage 1 (VLA grounding) and Stage 2 (navigation, retrieval, routing).

Illustrative Scenario

As a concrete instantiation of the capabilities above, we are preparing a campus-safety scenario, "Campus Keeper." A campus control room observes multiple CCTV feeds to recognize situations—search-and-rescue, emergency/accident response, and lost-item retrieval—and issues natural-language commands; the robot then classifies the situation and routes the case to a designated destination such as the security office, the health center, or the lost-and-found bin (misdelivery incurs an automatic penalty). Visually identical safety-mascot dolls (a single 3D asset) stand in for the subjects of interest, so the correct answer must be discerned from placement and context rather than appearance—validating safety responses without modeling real persons and thereby avoiding privacy concerns.

The specific mission design will be finalized during workshop preparation; the platform and scoring framework in §3.4–3.5 remain fixed regardless of the final scenario.

Review Process

• Finalist selection: finalists are selected by combining the platform's automated quantitative evaluation (objective Stage 1/Stage 2 scores) with a review of the workshop papers describing each team's approach.
• Final ranking: the final ranking is determined by the finalist teams' on-site presentations and live demonstrations at MetaCom.

Resources Required

The workshop is a full-day session—live-demo rehearsal in the morning and finals in the afternoon—with up to four on-site finalist teams. The program comprises finalist paper-presentation and live-demonstration sessions and one invited keynote. Required facilities are a projector and screen for presentations and network/display for live demonstrations. Sejong University provides the simulation platform and evaluation servers, while teams develop in their own environments.

Important dates

• 2026-07-01 – 2026-07-31

  Platform release · Registration opens together with platform release

• 2026-09-18

  Result submission (one-week extension option until 2026-09-25)

• around 2026-10-09

  Finalist announcement (reflects quantitative + paper review)

• 2026-11-06 – 2026-11-10

  Finals (IEEE MetaCom 2026), Xi'an, China — on-site presentation + live demonstration

Previous Editions

MARC is a competition hosted by Sejong University; its first edition was held in 2025. The inaugural MARC 2025 ran a campus waste-sorting (recycling) mission based on object detection; eight teams applied, and two teams ultimately earned the honor of winning the first edition.

MARC 2026 builds on this as the second edition, extending the task to VLA grounding, and is being organized with the goal of establishing MARC as an annually held event.

Official website: https://metasejong-competition.github.io/en/

Student Challenge link: https://marc-challenge.github.io/en/

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