Overview
The physical world is inherently non-rigid and dynamic. However, many modern robotic modeling and perception stacks assume rigid and static environments, limiting their robustness and generality in the real world. Non-rigid objects such as ropes, cloth, plants, and soft containers are common in daily life, and many environments, including sand, fluids, flexible structures, and dynamic scenes, exhibit deformability and history-dependency that challenges traditional assumptions in robotics.
This workshop comes at a pivotal moment: advances in foundation models, scalable data collection, differentiable physics, and 3D modeling and reconstruction create new opportunities to represent and interact in non-rigid, dynamic worlds. At the same time, real-world applications increasingly demand systems for handling soft, articulated, or granular dynamic objects. The workshop will convene researchers from robotics, computer vision, and machine learning to tackle shared challenges in perception, representation, and interaction in non-rigid worlds. By surfacing emerging solutions and promoting cross-disciplinary collaboration, the workshop aims to advance the development of more generalizable models grounded in data and physics for real-world robotic interaction.
Discussion Topics
- How might we learn to robustly perceive, reconstruct, and represent non-rigid objects in 3D, particularly from sparse or noisy sensor data?
- How might simulation tools, foundation models, and scene-specific reconstruction methods (e.g., 3D Gaussian splatting) be used to represent non-rigid, dynamic worlds?
- How might we actively, interactively, or adaptively perceive the world to reveal highly-uncertain, history-dependent object or environment properties?
- How might we achieve reliable robot manipulation and interaction in complex, real-world scenarios involving non-rigid objects with varying topology, material properties, and appearance?
- How might we design representation and perception strategies to handle complex object appearance or material properties such as translucency (e.g., glass), high reflectance (e.g., metal), or particulate behavior (e.g., sand)?
Invited Speakers
Rika Antonova
University of Cambridge
Jeannette Bohg
Stanford University
Yunzhu Li
Columbia UniversityEvent Schedule (tentative)
| 9:30 - 9:35 | Introduction and Opening Remarks |
| 9:35 - 10:00 | Speaker 1 |
| 10:00 - 10:30 | Spotlight Session 1 & Poster Overview |
| 10:30 - 11:00 | Coffee Break & Poster Sessions |
| 11:00 - 11:25 | Speaker 2 |
| 11:25 - 11:35 | Spotlight Session 2 |
| 11:35 - 12:00 | Speaker 3 |
| 12:00 - 12:30 | Panel Discussion & Debate |
Organizers
Holly Dinkel
University of Illinois
Marcel Büsching
KTH Royal Institute of Technology
Jad Abou-Chakra
Queensland University of Technology
Alessio Caporali
University of Bologna
Bardienus Pieter Duisterhof
Carnegie Mellon University
Alberta Longhini
KTH Royal Institute of Technology
Jens Lundell
KTH Royal Institute of Technology
Priya Sundaresan
Stanford University
Mingrui Yu
Tsinghua University