Introduction

Recent advancements in neural rendering techniques, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown great promise in creating photorealistic 3D scene reconstructions from real-world data like monocular images and videos. These techniques open up exciting new possibilities for robot learning by allowing robots to be trained in highly realistic simulated environments derived from real-world scenes. This emerging “real-to-sim” approach aims to close the gap between simulation and reality by transitioning from real-world data to reconstructed neural representations, training robots in these realistic environments, and then deploying them back into the real world with a minimized sim-to-real gap.

News

Workshop website is launched.

April 15, 2025

Schedule

The workshop will take place on 12 June 2025 from 13:45 - 17:25 CDT.

In the talk session, we will engage different perspectives from experts from diverse communities. In the panel session, we will raise several acute questions that bring debates from different views.

13:45 - 14:00 Welcome & Introduction
14:00 - 14:30 Keynote Talk 1 - Marco Pavone: Building Physical AI with Foundation-Model-Driven Closed-Loop Simulation
14:30 - 15:00 Keynote Talk 2 - Dhruv Shah: Evaluating and Improving Steerability of Generalist Robot Policies
15:00 - 15:30 Thematic Talk - Wayne Wu: Scaling-up Urban Simulation for Autonomous Micro-mobility
15:30 - 16:00 Oral Presentations: Sicheng Mo, Ruohan Zhang, Carmelo (Carlo) Sferrazza
16:00 - 16:30 Keynote Talk 3 - Gordon Wetzstein: Long-context and controllable video world models
16:30 - 17:00 Keynote Talk 4 - Lingjie Liu: Towards Next-Gen 3D Reconstruction and Generation: From Visual Fidelity to Multimodal and Physical Understanding
17:00 - 17:25 Panel Discussion