Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use:
bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded …

Scene representation has been a crucial design choice in robotic manipulation systems. An
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …

Humans use all of their senses to accomplish different tasks in everyday activities. In
contrast, existing work on robotic manipulation mostly relies on one, or occasionally two …

Learning predictive models from observations using deep neural networks (DNNs) is a
promising new approach to many real-world planning and control problems. However …

Effective planning of long-horizon deformable object manipulation requires suitable
abstractions at both the spatial and temporal levels. Previous methods typically either focus …

The concept of world models has garnered significant attention due to advancements in
multimodal large language models such as GPT-4 and video generation models such as …

Manipulation of elastoplastic objects like dough often involves topological changes such as
splitting and merging. The ability to accurately predict these topological changes that a …

Combining gradient-based trajectory optimization with differentiable physics simulation is an
efficient technique for solving soft-body manipulation problems. Using a well-crafted …

Scene representation is a crucial design choice in robotic manipulation systems. An ideal
representation is expected to be 3D, dynamic, and semantic to meet the demands of diverse …

Dynamics models learned from visual observations have shown to be effective in various
robotic manipulation tasks. One of the key questions for learning such dynamics models is …