ICRA - Generative Digital Twins

About
This workshop will explore generative digital twin technologies as a pathway to overcoming the persistent gaps between reality and simulation (Real2Sim) and simulation and reality (Sim2Real) in robotics. Despite advances in transfer learning and physics-based simulation, robot policies often underperform when deployed in the real world due to unmodeled dynamics, sensory mismatches, and environmental complexity. Digital twins - high-fidelity, adaptive virtual representations of robots and environments - offer a powerful approach to bridging these divides.

We will focus on generative digital twins, where AI-driven models automatically construct, update, and diversify twins to enhance robustness, adaptability, and scalability. Topics will include: (i) pipelines for digital twin generation (Real2Sim); (ii) benchmarks and case studies for Sim2Real transfer; (iii) learning algorithms leveraging adaptive twins; and (iv) applications across industrial, household, and mobile robotics. The workshop will bring together leading researchers from robotics, machine learning, and computer graphics, fostering interdisciplinary exchange through invited talks, poster sessions, and panel discussions. By highlighting recent advances and charting future directions, this event aims to define a roadmap for how generative digital twins can enable more reliable and generalizable robotic intelligence: Digital Twin Generation from Visual Data: A Survey

Schedule June 1st, 2026 - Vienna, Austria

Time		Info
9:00		Organizers: Introductory Remarks
9:30		Keynote: Ingmar Posner Oxford Title: blank Abstract blank
10:00		Keynote: Oier Mees Microsoft Title: blank Abstract blank
10:30		Coffee break
11:00		Keynote: Yuke Zhu NVIDIA, University of Texas at Austin Title: Training and Evaluating Generalist Policies with Digital Twins and Digital Cousins Abstract blank
11:30		Keynote: Jiajun Wu Stanford Title: Physical Grounding of Generative Digital Twins for Robotics Abstract Much of our visual world has its intrinsic, physical structure: scenes are made of objects; objects have their geometry, texture, material, and physical properties. With the rapid development of neural visual generative models, what role does such structural information play, or do we still need it at all? In this talk, I will discuss our recent efforts to build generative digital twins that are not only visually realistic, but also physically grounded. Beyond reconstruction and generation, I will also demonstrate how these objects can bridge the sim-to-real gap for robotic manipulation.
12:00		Spotlight talks
12:30		Lunch & Poster session
14:00		Keynote: Manolis Savva SFU Title: Digital Twins for Embodied AI: Advancing the Frontier of Realism and Interaction Abstract blank
14:30		Keynote: Hengshuang Zhao The University of Hong Kong Title: blank Abstract blank
15:00		Keynote: Ken Goldberg UC Berkeley Title: blank Abstract blank
15:30		Coffee Break
16:00		Discussion. Panelists: Ingmar Posner, Oier Mees, Yuke Zhu, Jiajun Wu, Manolis Savva, Hengshuang Zhao, Ken Goldberg
17:00		Organizers: Closing Remarks

Call for Papers

Targeted Topics

The gDT-IV Workshop at ICRA 2026 welcomes submissions on all aspects of generating digital twins from images and videos, especially (but not limited to):

Real2Sim pipelines for automatically constructing and updating digital twins from multimodal robot data (e.g., RGB-D, point clouds, logs, demonstrations).
Sim2Real transfer methods that leverage adaptive digital twins for policy learning, control, and planning in robotics.
Hybrid physics-based and generative models to reduce dynamics, sensing, and appearance gaps between simulation and reality.
Benchmarks, datasets, and evaluation protocols for Real2Sim and Sim2Real using digital twins in realistic robotic settings.
Learning algorithms that use continuously updated twins for robust, scalable, and generalizable robot behavior.
Applications of generative digital twins in industrial, household, and mobile/field robotics (manipulation, navigation, human–robot interaction, etc.).
Scalable simulation and scenario generation frameworks built around fleet-level or environment-level digital twins.
Safety, robustness, monitoring, and uncertainty modeling with digital twins in closed-loop robot operation.
Cross-disciplinary approaches from computer graphics, vision, and machine learning applied to digital twins for robotics.

Submission Information

We welcome Non-Archival contributions, submitted via the OpenReview portal. Specifically, we welcome a wide range of contributions including extended abstracts & short papers (up to 8 pages, suitable for work in progress, negative results, or position papers; references and appendices may extend beyond 8 pages) and previously published work (including papers accepted at ICRA 2026 or other venues in the last year). All submissions should be formatted with the ICRA 2026 Author Kit. Submissions remain double-blind for review but will not appear in proceedings. Thus, papers that have already been published at major conferences are also welcome. Authors may also submit their work to future conferences or journals after acceptance to this workshop.

Submit via OpenReview Real2Sim2Real Submission Portal

Submission Instructions

Authors planning to submit are strongly encouraged to create their OpenReview profiles well in advance of the submission deadline.

Please note OpenReview's moderation policy for newly created accounts:

Profiles created without an institutional email address will undergo a moderation process that may take up to two weeks before activation.
Profiles created with an institutional email address are typically activated automatically.

To avoid last-minute issues with submission access, we recommend registering early and, when possible, using an institutional email address.

Important Dates

Submission Deadline: March 22, 2026 (AOE)
Author Notifications: April 12, 2026 (AOE)
Camera-Ready: April 19, 2026 (AOE)